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zWd+T~(7`>=3_>ga1!jH+d}%P1gaH4ygUsxM2bUcECVu}1XTGpg^>e)!}RCo z%?Lm1iXL}k;}NUU8XA?D-cYJs@t*bP$ixot9Hd$Ub0fvzs=;d7oDN4{8)=LRm#6LIN~O{AAuk=*%%(VkuCKo#g)m<1g+*$_hO3d%J5uL*_htRt>>l z!4(R>nZ?lE=C(?+MgqqNZs<&_TPP2d$Um@u4AFHV;U+V`17zIh@+f zel@ljNM4|s&(LH!;^htjeGEh}@Cvuf;pjh3PCkom-21Vc0$x|6b`pzFI-(JmDD$gN zNl}jmekm4tt0zTU^`Ig6{Pk;J81>^PqvPb{q#y6Yt}!e*MeQ@9k$EFy{`YThJu@@@ z9=YRya)=Nh`D1kdB-|PO)?LTkSzTx*9syd6&B*$Hfo@3+Tl34RTD zdJyKpd5joeJA=$fiC>s4iAG$&mqECa=TNKLJCLn_k9P;j#QR^q=wm9s!T^@Hwcnqb z{~p8cw&z%j~va2LM6Z!7n}WO2Hdz>=8t5 zetV+xz7wX5xPzCwUsQA+>}W@P@Sz=S$iTcitSyr}%&{O&?&g6IyrVSu$mzMGS-h}o z<6b_!Tu=k}4O=iivYBC2T&4><2O!jj(`2kW$Gvsz=0kEf;q0@8U63xMgNgv{#pG*{ z(8K~({g>|(#hAyRJPtwKCK_%E5xhKv$L$?-*5|!{PCR?i#$4vNa`?`)#|P6oQ!QUYIEY$AGyh#aIAZZX z8JLLV`Tt*PT8YCXZ7>GtRVGr)(Q7@SR^{Z~-Crtr{NzcPo5aNYJ`9+cfvFWZsFU!# zJ%$>6dVlt%Qs0bUAm+T%e?kJAZSJ^1uVvjGM%(2L%t z0geS^_L=E;+)#@67yT|#gwDbZ2MRJ^?UkqvXFQg@jMvt6UeD6 z^WC&XW@9{zPqt@(2o8nJ?LoryLgUi+55=cO=kM0l)wzvW`piNO+6D;$)Z4PE>`va0 zR-KSTUzP|dgGhi!zm2#slg0o^l#_iA!W4qZ2XmkS-wfRMI5@zPSN;Hzs0Cg%8qp@% z+C)MkM9tj6*%@t9{0$fcWKahY#!l3>vmqlc_1IcY=za;e!6Ka$r~vhrlb46kt2*%M zEJA{hcY@Vml7f_!R5qVRKm&%JFhX&0bv1l^?LF|?Ly_zdNXzLeC? z5rg9F)r7=WE`&IvaZz-L8mR6$q4ureL`asF8MN=HnI=ZcB#uSu1Sk}&yh*Ja2U7HV z_jAEhY7@-h41@#kL4q@48YWBukK;Z3Wf-#JkFTP!HB6|PJ8$VX;5+n({2kz$fCcA> zOt#p7DO||(Bp4Efhtf=!pfs;`ZYjgG{l41-Nk)r^7IJGt&YKtGCttKot-1@aW6JHn zEIhuYp#6cYIfMxYl4r4yDM|$$K`IrCq-X$fLaKrs-IuQHeWON#m*c&x;~1c_DGZ1l z_8uRSBRL+T@QIMI41Bqf>k)X$X9U(DCvWTE2x)FZVGZhYlueDm;y}t1e>UTTXTqtq zo55lrPBjNm0w^KugamkdStJle#6^+dtrmpE3_NL0Ui0+Huq!\n", "# Intro to TensorFlow for Deep Learning" ] }, diff --git a/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb b/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb index 1032e8b2ec..c78ae0e880 100644 --- a/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb +++ b/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb @@ -93,7 +93,7 @@ "\n", "# Load data\n", "mnistDf_backup = pd.read_csv(\n", - " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n", + " \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/mnist_train_small.csv\",\n", " sep=\",\",\n", " header=None)\n", "# Shuffle data\n", diff --git a/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb b/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb index 3dfb5f6621..57b6410fe2 100644 --- a/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb +++ b/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb @@ -92,7 +92,7 @@ "pd.options.display.float_format = '{:.1f}'.format\n", "\n", "wineDf = pd.read_csv(\n", - " \"https://download.mlcc.google.com/mledu-datasets/winequality.csv\",\n", + " \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/winequality.csv\",\n", " encoding='latin-1')\n", "wineDf.columns = ['fixed acidity','volatile acidity','citric acid',\n", " 'residual sugar','chlorides','free sulfur dioxide',\n", diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb index 4356f6dc77..20ac9dfa66 100644 --- a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb +++ b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7f8c5d9c", + "id": "ffe91a20", "metadata": {}, "source": [ "# Create a regression model\n", diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb index 28b6e7d612..84528f3f83 100644 --- a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb +++ b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "54030063", + "id": "f38f7776", "metadata": {}, "source": [ "# Exploring visualizations\n", diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb index df47a8e2fd..4b98510e42 100644 --- a/_sources/assignments/ml-fundamentals/parameter-play.ipynb +++ b/_sources/assignments/ml-fundamentals/parameter-play.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "1407fd89", + "id": "1798d5b8", "metadata": {}, "source": [ "# Parameter play\n", diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb index 9ea709ddd9..0e11347d46 100644 --- a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb +++ b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6f86010f", + "id": "00646e5e", "metadata": {}, "source": [ "# Regression with Scikit-learn\n", diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb index a8a8404cd2..fcfe85775e 100644 --- a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb +++ b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "63285526", + "id": "6a8e2a7e", "metadata": {}, "source": [ "# Retrying some regression\n", diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb index 3b38e3b086..2bc65be175 100644 --- a/_sources/data-science/working-with-data/numpy.ipynb +++ b/_sources/data-science/working-with-data/numpy.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b5888482", + "id": "afd632b9", "metadata": {}, "source": [ "# NumPy\n", @@ -19,7 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "785cda3a", + "id": "c0e30daf", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "4497576e", + "id": "57e00754", "metadata": {}, "source": [ "### Create a basic array\n", @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a102ea7f", + "id": "53d4f97b", "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "45e91322", + "id": "96b27b37", "metadata": {}, "source": [ "Besides creating an array from a sequence of elements, you can easily create an array filled with `0`’s:" @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "dfe4c7a0", + "id": "90cf63b7", "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ }, { "cell_type": "markdown", - "id": "03a6c41b", + "id": "1308eae0", "metadata": {}, "source": [ "Or an array filled with 1’s:" @@ -113,7 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "30524d08", + "id": "4a492738", "metadata": {}, "outputs": [ { @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "ebf9858c", + "id": "3f580af4", "metadata": {}, "source": [ "Or even an empty array! The function `empty` creates an array whose initial content is random and depends on the state of the memory. The reason to use `empty` over `zeros` (or something similar) is speed - just make sure to fill every element afterwards!" @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "4e02ccfd", + "id": "9db1f151", "metadata": {}, "outputs": [ { @@ -162,7 +162,7 @@ }, { "cell_type": "markdown", - "id": "3dbd97c7", + "id": "6b3857db", "metadata": {}, "source": [ "You can create an array with a range of elements:" @@ -171,7 +171,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "a0448381", + "id": "09ae9658", "metadata": {}, "outputs": [ { @@ -191,7 +191,7 @@ }, { "cell_type": "markdown", - "id": "47cada4e", + "id": "a24a8fce", "metadata": {}, "source": [ "And even an array that contains a range of evenly spaced intervals. To do this, you will specify the **first number**, **last number**, and the **step size**." @@ -200,7 +200,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "05de6608", + "id": "c771bd4e", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "69708ef5", + "id": "f82cbadc", "metadata": {}, "source": [ "You can also use `np.linspace()` to create an array with values that are spaced linearly in a specified interval:" @@ -229,7 +229,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "73c75e6d", + "id": "8b00a93d", "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "d4ed5418", + "id": "4eac2b2c", "metadata": {}, "source": [ "While the default data type is floating point (`np.float64`), you can explicitly specify which data type you want using the `dtype` keyword." @@ -258,7 +258,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "03b5f43a", + "id": "fa5ec2d3", "metadata": {}, "outputs": [ { @@ -278,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "e5709a38", + "id": "d0818c5e", "metadata": {}, "source": [ "### Adding, removing, and sorting elements\n", @@ -291,7 +291,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "049e020a", + "id": "1bea9026", "metadata": {}, "outputs": [], "source": [ @@ -300,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "be71fbe1", + "id": "627bdbdc", "metadata": {}, "source": [ "You can quickly sort the numbers in ascending order with:" @@ -309,7 +309,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "de2aa8cf", + "id": "8833b143", "metadata": {}, "outputs": [ { @@ -329,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "d6849175", + "id": "0fb253ea", "metadata": {}, "source": [ "In addition to sort, which returns a sorted copy of an array, you can use:\n", @@ -345,7 +345,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "76837ee3", + "id": "e26b89ca", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "28bb0702", + "id": "2c76cd24", "metadata": {}, "source": [ "You can concatenate them with `np.concatenate()`." @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "0f172ac3", + "id": "34f3e25f", "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "ad8a9627", + "id": "91c6a0aa", "metadata": {}, "source": [ "Or, if you start with these arrays:" @@ -393,7 +393,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b3d5e201", + "id": "221bb0f1", "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ }, { "cell_type": "markdown", - "id": "38230d50", + "id": "e88c5796", "metadata": {}, "source": [ "You can concatenate them with:" @@ -412,7 +412,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "dcc748c4", + "id": "7cfe1084", "metadata": {}, "outputs": [ { @@ -434,7 +434,7 @@ }, { "cell_type": "markdown", - "id": "b9aed6b6", + "id": "135cf70b", "metadata": {}, "source": [ "In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.\n", @@ -450,7 +450,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "930d5c5a", + "id": "f6507c6a", "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "6de83562", + "id": "2e5a343a", "metadata": {}, "outputs": [ { @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "910fa2fa", + "id": "d58138bd", "metadata": {}, "source": [ "- ndarray.shape\n", @@ -505,7 +505,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "4df0897a", + "id": "1321a855", "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "a8b7089e", + "id": "df14a749", "metadata": {}, "source": [ "- ndarray.size\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "4b82bdeb", + "id": "92317290", "metadata": {}, "outputs": [ { @@ -555,7 +555,7 @@ }, { "cell_type": "markdown", - "id": "c46827ca", + "id": "94e57081", "metadata": {}, "source": [ "- ndarray.dtype\n", @@ -565,7 +565,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "1a4fdec5", + "id": "d765a8dc", "metadata": {}, "outputs": [ { @@ -586,7 +586,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "46cb5a92", + "id": "32a250c9", "metadata": {}, "outputs": [ { @@ -606,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "63c6eebf", + "id": "e897d24c", "metadata": {}, "source": [ "- ndarray.itemsize\n", @@ -616,7 +616,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "2d8c0e24", + "id": "53bbe240", "metadata": {}, "outputs": [ { @@ -636,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "6560f96f", + "id": "18d7a014", "metadata": {}, "source": [ "- ndarray.data\n", @@ -646,13 +646,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "88e890a3", + "id": "684a1fdc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -666,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "c3608ecd", + "id": "84baa9ec", "metadata": {}, "source": [ "### Reshape an array\n", @@ -679,7 +679,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "283a3027", + "id": "476ccc9d", "metadata": {}, "outputs": [ { @@ -700,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "f77a9c2e", + "id": "18d73378", "metadata": {}, "source": [ "You can use `reshape()` to reshape your array. For example, you can reshape this array to an array with three rows and two columns:" @@ -709,7 +709,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "27b6a6f4", + "id": "fd56c9d1", "metadata": {}, "outputs": [ { @@ -732,7 +732,7 @@ }, { "cell_type": "markdown", - "id": "4f9818e1", + "id": "aacbe93f", "metadata": {}, "source": [ "With `np.reshape`, you can specify a few optional parameters:" @@ -741,7 +741,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "2b0ab3e9", + "id": "efbbeaa3", "metadata": {}, "outputs": [ { @@ -761,7 +761,7 @@ }, { "cell_type": "markdown", - "id": "ec4b3d99", + "id": "57483977", "metadata": {}, "source": [ "`a` is the array to be reshaped.\n", @@ -782,7 +782,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "c83a127f", + "id": "24927b0b", "metadata": {}, "outputs": [ { @@ -803,7 +803,7 @@ }, { "cell_type": "markdown", - "id": "fb4550e1", + "id": "a86da884", "metadata": {}, "source": [ "You can use `np.newaxis` to add a new axis:" @@ -812,7 +812,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "343b677a", + "id": "dcca9748", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "0b886e81", + "id": "a6d1a5e4", "metadata": {}, "source": [ "You can explicitly convert a 1D array with either a row vector or a column vector using `np.newaxis`. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:" @@ -842,7 +842,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "2396425f", + "id": "b671df03", "metadata": {}, "outputs": [ { @@ -863,7 +863,7 @@ }, { "cell_type": "markdown", - "id": "e88e5084", + "id": "8a03741d", "metadata": {}, "source": [ "Or, for a column vector, you can insert an axis along the second dimension:" @@ -872,7 +872,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "f86eaec4", + "id": "69c62c9c", "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ }, { "cell_type": "markdown", - "id": "c2ceeaa3", + "id": "deeed2bc", "metadata": {}, "source": [ "You can also expand an array by inserting a new axis at a specified position with `np.expand_dims`.\n", @@ -904,7 +904,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "feaaf5f5", + "id": "1154f985", "metadata": {}, "outputs": [ { @@ -925,7 +925,7 @@ }, { "cell_type": "markdown", - "id": "8179a047", + "id": "bb445792", "metadata": {}, "source": [ "You can use np.expand_dims to add an axis at index position 1 with:" @@ -934,7 +934,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "f2395964", + "id": "64df29e0", "metadata": {}, "outputs": [ { @@ -955,7 +955,7 @@ }, { "cell_type": "markdown", - "id": "e731906e", + "id": "6dcef3f0", "metadata": {}, "source": [ "You can add an axis at index position 0 with:" @@ -964,7 +964,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "1e831b21", + "id": "ccc24c7a", "metadata": {}, "outputs": [ { @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "32c57fe6", + "id": "d640e522", "metadata": {}, "source": [ "### Indexing and slicing\n", @@ -996,7 +996,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "e6828924", + "id": "c91dbaea", "metadata": {}, "outputs": [], "source": [ @@ -1006,7 +1006,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "fcf6182e", + "id": "f1a45f5b", "metadata": {}, "outputs": [ { @@ -1027,7 +1027,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "db06cad3", + "id": "a276b0ac", "metadata": {}, "outputs": [ { @@ -1048,7 +1048,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "77460fae", + "id": "ff2a4b3e", "metadata": {}, "outputs": [ { @@ -1069,7 +1069,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "50d3d7d5", + "id": "8c44de1c", "metadata": {}, "outputs": [ { @@ -1089,7 +1089,7 @@ }, { "cell_type": "markdown", - "id": "390f7286", + "id": "793dc947", "metadata": {}, "source": [ "You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.\n", @@ -1102,7 +1102,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "bfabe582", + "id": "6b0d7201", "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1111,7 @@ }, { "cell_type": "markdown", - "id": "98d7b447", + "id": "66dc4c1a", "metadata": {}, "source": [ "You can easily print all of the values in the array that are less than 5." @@ -1120,7 +1120,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "7b481bbb", + "id": "466f59e3", "metadata": {}, "outputs": [ { @@ -1140,7 +1140,7 @@ }, { "cell_type": "markdown", - "id": "a1f20041", + "id": "60fd21ac", "metadata": {}, "source": [ "You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array." @@ -1149,7 +1149,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "abbbef5c", + "id": "57caa999", "metadata": {}, "outputs": [ { @@ -1170,7 +1170,7 @@ }, { "cell_type": "markdown", - "id": "5f9efe6e", + "id": "f7f8b40a", "metadata": {}, "source": [ "You can select elements that are divisible by 2:" @@ -1179,7 +1179,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "6fcf8c82", + "id": "b2b5e8a7", "metadata": {}, "outputs": [ { @@ -1200,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "ac07b8bf", + "id": "aeb7292c", "metadata": {}, "source": [ "Or you can select elements that satisfy two conditions using the `&` and `|` operators:" @@ -1209,7 +1209,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "a6c02448", + "id": "1fd64587", "metadata": {}, "outputs": [ { @@ -1230,7 +1230,7 @@ }, { "cell_type": "markdown", - "id": "9aea32e3", + "id": "29f3f751", "metadata": {}, "source": [ "You can also make use of the logical operators `&` and `|` in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values." @@ -1239,7 +1239,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "52e1a2a9", + "id": "73677cf5", "metadata": {}, "outputs": [ { @@ -1262,7 +1262,7 @@ }, { "cell_type": "markdown", - "id": "6196ee04", + "id": "4a44e4eb", "metadata": {}, "source": [ "You can also use `np.nonzero()` to select elements or indices from an array.\n", @@ -1273,7 +1273,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "84de3fc8", + "id": "74838c5e", "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "62b27c24", + "id": "15fba768", "metadata": {}, "source": [ "You can use `np.nonzero()` to print the indices of elements that are, for example, less than 5:" @@ -1291,7 +1291,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "bf9b79b9", + "id": "ee023e21", "metadata": {}, "outputs": [ { @@ -1312,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "be13baec", + "id": "13a0f028", "metadata": {}, "source": [ "In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.\n", @@ -1323,7 +1323,7 @@ { "cell_type": "code", "execution_count": 47, - "id": "6e50ff11", + "id": "1d487b02", "metadata": {}, "outputs": [ { @@ -1345,7 +1345,7 @@ }, { "cell_type": "markdown", - "id": "2eecc043", + "id": "c95ed28e", "metadata": {}, "source": [ "You can also use `np.nonzero()` to print the elements in an array that are less than 5 with:" @@ -1354,7 +1354,7 @@ { "cell_type": "code", "execution_count": 48, - "id": "e6439bbf", + "id": "e428d9da", "metadata": {}, "outputs": [ { @@ -1374,7 +1374,7 @@ }, { "cell_type": "markdown", - "id": "85a29814", + "id": "38065886", "metadata": {}, "source": [ "If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:" @@ -1383,7 +1383,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "454088ea", + "id": "3a5553af", "metadata": {}, "outputs": [ { @@ -1404,7 +1404,7 @@ }, { "cell_type": "markdown", - "id": "8be2d548", + "id": "b20e0feb", "metadata": {}, "source": [ "### Create an array from existing data\n", @@ -1417,7 +1417,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "62890d3d", + "id": "bf17f95a", "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "e7bdf8cb", + "id": "eef16df9", "metadata": {}, "source": [ "You can create a new array from a section of your array any time by specifying where you want to slice your array." @@ -1435,7 +1435,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "cf1bb50c", + "id": "5e32a71d", "metadata": {}, "outputs": [ { @@ -1456,7 +1456,7 @@ }, { "cell_type": "markdown", - "id": "b4a69e71", + "id": "046dc652", "metadata": {}, "source": [ "Here, you grabbed a section of your array from index position 3 through index position 8.\n", @@ -1467,7 +1467,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "e8d3491c", + "id": "d1ff94b9", "metadata": {}, "outputs": [], "source": [ @@ -1479,7 +1479,7 @@ }, { "cell_type": "markdown", - "id": "a1fd82bb", + "id": "21ff4cea", "metadata": {}, "source": [ "You can stack them vertically with `vstack`:" @@ -1488,7 +1488,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "2b133e0b", + "id": "f19ecc4a", "metadata": {}, "outputs": [ { @@ -1511,7 +1511,7 @@ }, { "cell_type": "markdown", - "id": "cb12b0c2", + "id": "83f58573", "metadata": {}, "source": [ "Or stack them horizontally with hstack:" @@ -1520,7 +1520,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "bce35ecd", + "id": "17e3f7ac", "metadata": {}, "outputs": [ { @@ -1541,7 +1541,7 @@ }, { "cell_type": "markdown", - "id": "e28a6f04", + "id": "5051bc67", "metadata": {}, "source": [ "You can split an array into several smaller arrays using `hsplit`. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.\n", @@ -1552,7 +1552,7 @@ { "cell_type": "code", "execution_count": 55, - "id": "f99bd667", + "id": "050776e1", "metadata": {}, "outputs": [ { @@ -1574,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "cbc08091", + "id": "a050b004", "metadata": {}, "source": [ "If you wanted to split this array into three equally shaped arrays, you would run:" @@ -1583,7 +1583,7 @@ { "cell_type": "code", "execution_count": 56, - "id": "14c2c436", + "id": "33a4af0d", "metadata": {}, "outputs": [ { @@ -1608,7 +1608,7 @@ }, { "cell_type": "markdown", - "id": "35b81ae5", + "id": "6b8ba061", "metadata": {}, "source": [ "If you wanted to split your array after the third and fourth column, you’d run:" @@ -1617,7 +1617,7 @@ { "cell_type": "code", "execution_count": 57, - "id": "dcaf2edb", + "id": "82cbb781", "metadata": {}, "outputs": [ { @@ -1642,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "c81861d9", + "id": "f9d630fb", "metadata": {}, "source": [ "You can use the `view` method to create a new array object that looks at the same data as the original array (a shallow copy).\n", @@ -1655,7 +1655,7 @@ { "cell_type": "code", "execution_count": 58, - "id": "234f0412", + "id": "5263cc58", "metadata": {}, "outputs": [], "source": [ @@ -1664,7 +1664,7 @@ }, { "cell_type": "markdown", - "id": "af503dcd", + "id": "3d2ebdeb", "metadata": {}, "source": [ "Now we create an array `b1` by slicing `a` and modify the first element of `b1`. This will modify the corresponding element in `a` as well!" @@ -1673,7 +1673,7 @@ { "cell_type": "code", "execution_count": 59, - "id": "845c5d61", + "id": "b165b69e", "metadata": {}, "outputs": [ { @@ -1695,7 +1695,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "106bbd4b", + "id": "47c9a96d", "metadata": {}, "outputs": [ { @@ -1717,7 +1717,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "a3cea45b", + "id": "b3f713e5", "metadata": {}, "outputs": [ { @@ -1739,7 +1739,7 @@ }, { "cell_type": "markdown", - "id": "09b0efa2", + "id": "767927aa", "metadata": {}, "source": [ "Using the `copy` method will make a complete copy of the array and its data (a deep copy). To use this on your array, you could run:" @@ -1748,7 +1748,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "e97bbc37", + "id": "c20fcc4b", "metadata": {}, "outputs": [], "source": [ @@ -1757,7 +1757,7 @@ }, { "cell_type": "markdown", - "id": "840ff748", + "id": "4d9ff5df", "metadata": {}, "source": [ "## Array operations\n", @@ -1770,7 +1770,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "d34c3785", + "id": "7020198e", "metadata": {}, "outputs": [], "source": [ @@ -1780,7 +1780,7 @@ }, { "cell_type": "markdown", - "id": "354b812c", + "id": "c31a33cd", "metadata": {}, "source": [ "You can add the arrays together with the plus sign." @@ -1789,7 +1789,7 @@ { "cell_type": "code", "execution_count": 64, - "id": "044a9d38", + "id": "246ef529", "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "ca6b8eee", + "id": "972acd1b", "metadata": {}, "source": [ "You can, of course, do more than just addition!" @@ -1818,7 +1818,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "02080dd2", + "id": "16c2a890", "metadata": {}, "outputs": [ { @@ -1839,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "06df329f", + "id": "a16dadd6", "metadata": {}, "source": [ "Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use `sum()`. This works for 1D arrays, 2D arrays, and arrays in higher dimensions." @@ -1848,7 +1848,7 @@ { "cell_type": "code", "execution_count": 66, - "id": "d5942646", + "id": "ed0c6159", "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ }, { "cell_type": "markdown", - "id": "de4ae5f1", + "id": "8965e1d3", "metadata": {}, "source": [ "To add the rows or the columns in a 2D array, you would specify the axis.\n", @@ -1880,7 +1880,7 @@ { "cell_type": "code", "execution_count": 67, - "id": "797ef075", + "id": "0b73d411", "metadata": {}, "outputs": [], "source": [ @@ -1889,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "d6326b81", + "id": "5c022264", "metadata": {}, "source": [ "You can sum over the axis of rows with:" @@ -1898,7 +1898,7 @@ { "cell_type": "code", "execution_count": 68, - "id": "879ed4b6", + "id": "e91c6613", "metadata": {}, "outputs": [ { @@ -1918,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "1d1d6798", + "id": "995174be", "metadata": {}, "source": [ "You can sum over the axis of columns with:" @@ -1927,7 +1927,7 @@ { "cell_type": "code", "execution_count": 69, - "id": "89742bc3", + "id": "d28ea010", "metadata": {}, "outputs": [ { @@ -1947,7 +1947,7 @@ }, { "cell_type": "markdown", - "id": "e05aea65", + "id": "5e161355", "metadata": {}, "source": [ "### Universal functions(ufunc)\n", @@ -2038,7 +2038,7 @@ { "cell_type": "code", "execution_count": 70, - "id": "2e7a2827", + "id": "32b883ba", "metadata": {}, "outputs": [ { @@ -2060,7 +2060,7 @@ }, { "cell_type": "markdown", - "id": "e14d53f4", + "id": "9ffb0ade", "metadata": {}, "source": [ "NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints. The simplest broadcasting example occurs when an array and a scalar value are combined in an operation:" @@ -2069,7 +2069,7 @@ { "cell_type": "code", "execution_count": 71, - "id": "3034ee65", + "id": "e795ee7c", "metadata": {}, "outputs": [ { @@ -2091,7 +2091,7 @@ }, { "cell_type": "markdown", - "id": "b32494fc", + "id": "d0dc4f1d", "metadata": {}, "source": [ "The result is equivalent to the previous example where `b` was an array. NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.\n", @@ -2133,7 +2133,7 @@ { "cell_type": "code", "execution_count": 72, - "id": "dfec7dfa", + "id": "9622f23e", "metadata": {}, "outputs": [ { @@ -2154,7 +2154,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "b109968d", + "id": "376131a7", "metadata": {}, "outputs": [ { @@ -2175,7 +2175,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "14573901", + "id": "f9b036f6", "metadata": {}, "outputs": [ { @@ -2195,7 +2195,7 @@ }, { "cell_type": "markdown", - "id": "827494ec", + "id": "50959069", "metadata": {}, "source": [ "Let’s start with this array, called “a”." @@ -2204,7 +2204,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "8fbcc6fd", + "id": "f2bba32b", "metadata": {}, "outputs": [], "source": [ @@ -2215,7 +2215,7 @@ }, { "cell_type": "markdown", - "id": "9bb857c3", + "id": "f62ca103", "metadata": {}, "source": [ "It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:" @@ -2224,7 +2224,7 @@ { "cell_type": "code", "execution_count": 76, - "id": "1365f7c7", + "id": "9e4268f9", "metadata": {}, "outputs": [ { @@ -2244,7 +2244,7 @@ }, { "cell_type": "markdown", - "id": "7d979114", + "id": "b6ab4168", "metadata": {}, "source": [ "Or:" @@ -2253,7 +2253,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "d01f3f34", + "id": "ba76b255", "metadata": {}, "outputs": [ { @@ -2273,7 +2273,7 @@ }, { "cell_type": "markdown", - "id": "6b75981d", + "id": "26f0a9aa", "metadata": {}, "source": [ "You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying `axis=0`." @@ -2282,7 +2282,7 @@ { "cell_type": "code", "execution_count": 78, - "id": "b877a6d2", + "id": "ed234ccc", "metadata": {}, "outputs": [ { @@ -2302,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "0f014088", + "id": "c53293e7", "metadata": {}, "source": [ "The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.\n", @@ -2325,7 +2325,7 @@ { "cell_type": "code", "execution_count": 79, - "id": "11a0d9b8", + "id": "91383c9a", "metadata": {}, "outputs": [], "source": [ @@ -2335,7 +2335,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "e5904a16", + "id": "ddb83fe6", "metadata": {}, "outputs": [ { @@ -2356,7 +2356,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "b1f20faa", + "id": "bf560db2", "metadata": {}, "outputs": [ { @@ -2376,7 +2376,7 @@ }, { "cell_type": "markdown", - "id": "78347e54", + "id": "cd1802ce", "metadata": {}, "source": [ "It is not necessary to separate each dimension’s index into its own set of square brackets." @@ -2385,7 +2385,7 @@ { "cell_type": "code", "execution_count": 82, - "id": "38396974", + "id": "6db67042", "metadata": {}, "outputs": [], "source": [ @@ -2395,7 +2395,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "14246c83", + "id": "8d33f82a", "metadata": {}, "outputs": [ { @@ -2416,7 +2416,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "d964c396", + "id": "eac24599", "metadata": {}, "outputs": [ { @@ -2436,7 +2436,7 @@ }, { "cell_type": "markdown", - "id": "f4db7183", + "id": "d72b521a", "metadata": {}, "source": [ "Note that If one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example:" @@ -2445,7 +2445,7 @@ { "cell_type": "code", "execution_count": 85, - "id": "b3063d7f", + "id": "be877af4", "metadata": {}, "outputs": [ { @@ -2465,7 +2465,7 @@ }, { "cell_type": "markdown", - "id": "302f6ed0", + "id": "64842e26", "metadata": {}, "source": [ "That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is a view, i.e., it is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is:" @@ -2474,7 +2474,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "b61a9fd7", + "id": "012d21ab", "metadata": {}, "outputs": [ { @@ -2494,7 +2494,7 @@ }, { "cell_type": "markdown", - "id": "35a67fd4", + "id": "cb1d7b6d", "metadata": {}, "source": [ "So note that `x[0, 2] == x[0][2]` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2.\n", @@ -2521,7 +2521,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "5d86e072", + "id": "4de2cbd9", "metadata": {}, "outputs": [ { @@ -2542,7 +2542,7 @@ }, { "cell_type": "markdown", - "id": "c642e2bd", + "id": "efb0fe8c", "metadata": {}, "source": [ "- Negative *i* and *j* are interpreted as *n + i* and *n + j* where *n* is the number of elements in the corresponding dimension. Negative *k* makes stepping go towards smaller indices. From the above example:" @@ -2551,7 +2551,7 @@ { "cell_type": "code", "execution_count": 88, - "id": "c98b0c5a", + "id": "93896f51", "metadata": {}, "outputs": [ { @@ -2572,7 +2572,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "3a57ee72", + "id": "abab0c27", "metadata": {}, "outputs": [ { @@ -2592,7 +2592,7 @@ }, { "cell_type": "markdown", - "id": "fbf154df", + "id": "bcfac7a9", "metadata": {}, "source": [ "- Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and *n - 1* for *k < 0*. If *j* is not given it defaults to *n* for *k > 0* and *-n-1* for *k < 0*. If *k* is not given it defaults to 1. Note that `::` is the same as : and means select all indices along this axis. From the above example:" @@ -2601,7 +2601,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "54fa2284", + "id": "20f1a2bc", "metadata": {}, "outputs": [ { @@ -2621,7 +2621,7 @@ }, { "cell_type": "markdown", - "id": "8cacdc50", + "id": "df45a1c8", "metadata": {}, "source": [ "- If the number of objects in the selection tuple is less than N, then `:` is assumed for any subsequent dimensions. For example:" @@ -2630,7 +2630,7 @@ { "cell_type": "code", "execution_count": 91, - "id": "a6a38346", + "id": "8fbec975", "metadata": {}, "outputs": [ { @@ -2652,7 +2652,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "45fd34c7", + "id": "592a1d4d", "metadata": {}, "outputs": [ { @@ -2674,7 +2674,7 @@ }, { "cell_type": "markdown", - "id": "bd80540c", + "id": "4dc1edac", "metadata": {}, "source": [ "- An integer, *i*, returns the same values as `i:i+1` **except** the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the *p*-th element an integer (and all other entries *:*) returns the corresponding sub-array with dimension *N - 1*. If *N = 1* then the returned object is an array scalar.\n", @@ -2699,7 +2699,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "cf487e2a", + "id": "70b88522", "metadata": {}, "outputs": [ { @@ -2720,7 +2720,7 @@ }, { "cell_type": "markdown", - "id": "cea60560", + "id": "fc28c761", "metadata": {}, "source": [ "This is equivalent to:" @@ -2729,7 +2729,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "256b0b53", + "id": "d8be64d9", "metadata": {}, "outputs": [ { @@ -2750,7 +2750,7 @@ }, { "cell_type": "markdown", - "id": "f0852ec8", + "id": "68ae62cf", "metadata": {}, "source": [ "Each `newaxis` object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension. The added dimension is the position of the `newaxis` object in the selection tuple. `newaxis` is an alias for `None`, and `None` can be used in place of this with the same result. From the above example:" @@ -2759,7 +2759,7 @@ { "cell_type": "code", "execution_count": 95, - "id": "f3949020", + "id": "e907ef93", "metadata": {}, "outputs": [ { @@ -2780,7 +2780,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "9c51768b", + "id": "dfc40517", "metadata": {}, "outputs": [ { @@ -2800,7 +2800,7 @@ }, { "cell_type": "markdown", - "id": "d342e127", + "id": "4c485aaf", "metadata": {}, "source": [ "This can be handy to combine two arrays in a way that otherwise would require explicit reshaping operations. For example:" @@ -2809,7 +2809,7 @@ { "cell_type": "code", "execution_count": 97, - "id": "adbe9075", + "id": "1fd64ba6", "metadata": {}, "outputs": [ { @@ -2834,7 +2834,7 @@ }, { "cell_type": "markdown", - "id": "b17566e0", + "id": "9bb6ec4c", "metadata": {}, "source": [ "### Advanced indexing\n", @@ -2857,7 +2857,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "02932a10", + "id": "063462f6", "metadata": {}, "outputs": [], "source": [ @@ -2867,7 +2867,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "7f8fdde1", + "id": "7a3e12f0", "metadata": {}, "outputs": [ { @@ -2888,7 +2888,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "a8a7ea68", + "id": "744d9514", "metadata": {}, "outputs": [ { @@ -2909,7 +2909,7 @@ { "cell_type": "code", "execution_count": 101, - "id": "5ccb1438", + "id": "b28b3408", "metadata": {}, "outputs": [ { @@ -2929,7 +2929,7 @@ }, { "cell_type": "markdown", - "id": "96d8e32b", + "id": "f8a9a31d", "metadata": {}, "source": [ "If the index values are out of bounds then an `IndexError` is thrown:" @@ -2938,7 +2938,7 @@ { "cell_type": "code", "execution_count": 102, - "id": "43a4fb4a", + "id": "2ad82631", "metadata": {}, "outputs": [], "source": [ @@ -2948,7 +2948,7 @@ { "cell_type": "code", "execution_count": 103, - "id": "f065ee6a", + "id": "0a5c9ed5", "metadata": {}, "outputs": [ { @@ -2969,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "e7c13101", + "id": "89ac205a", "metadata": {}, "source": [ "```py\n", @@ -2999,7 +2999,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "063101bc", + "id": "875bf1de", "metadata": {}, "outputs": [], "source": [ @@ -3009,7 +3009,7 @@ { "cell_type": "code", "execution_count": 105, - "id": "e4a0954b", + "id": "60517bde", "metadata": {}, "outputs": [ { @@ -3034,7 +3034,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "f5399375", + "id": "bd10806d", "metadata": {}, "outputs": [ { @@ -3054,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "54a9e869", + "id": "1555bcc5", "metadata": {}, "source": [ "In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is `y[0, 0]`. The next value is `y[2, 1]`, and the last is `y[4, 2]`.\n", @@ -3077,7 +3077,7 @@ { "cell_type": "code", "execution_count": 107, - "id": "b5f16305", + "id": "98938b31", "metadata": {}, "outputs": [ { @@ -3097,7 +3097,7 @@ }, { "cell_type": "markdown", - "id": "31c71ff4", + "id": "d8d6cd40", "metadata": {}, "source": [ "Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y:" @@ -3106,7 +3106,7 @@ { "cell_type": "code", "execution_count": 108, - "id": "c844ded4", + "id": "cb5a9daf", "metadata": {}, "outputs": [ { @@ -3128,7 +3128,7 @@ }, { "cell_type": "markdown", - "id": "ac086385", + "id": "2477d757", "metadata": {}, "source": [ "It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).\n", @@ -3143,7 +3143,7 @@ { "cell_type": "code", "execution_count": 109, - "id": "0570bd1c", + "id": "0c0c1d55", "metadata": {}, "outputs": [ { @@ -3164,7 +3164,7 @@ }, { "cell_type": "markdown", - "id": "a3dcf6fe", + "id": "b413f935", "metadata": {}, "source": [ "To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function `ix_` can help with this broadcasting. This is best understood with an example.\n", @@ -3177,7 +3177,7 @@ { "cell_type": "code", "execution_count": 110, - "id": "9bafc89a", + "id": "2b7d821f", "metadata": {}, "outputs": [ { @@ -3206,7 +3206,7 @@ }, { "cell_type": "markdown", - "id": "e2c55b65", + "id": "4175b1f5", "metadata": {}, "source": [ "However, since the indexing arrays above just repeat themselves, broadcasting can be used (compare operations such as `rows[:, np.newaxis] + columns`) to simplify this:" @@ -3215,7 +3215,7 @@ { "cell_type": "code", "execution_count": 111, - "id": "7c316750", + "id": "9c76050f", "metadata": {}, "outputs": [], "source": [ @@ -3226,7 +3226,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "15afb73a", + "id": "5b1e4f8b", "metadata": {}, "outputs": [ { @@ -3248,7 +3248,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "30c1524d", + "id": "0dc54de3", "metadata": {}, "outputs": [ { @@ -3269,7 +3269,7 @@ }, { "cell_type": "markdown", - "id": "90a1e37e", + "id": "e371c00b", "metadata": {}, "source": [ "This broadcasting can also be achieved using the function `ix_`:" @@ -3278,7 +3278,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "c1cc2872", + "id": "4efe4cd1", "metadata": {}, "outputs": [ { @@ -3299,7 +3299,7 @@ }, { "cell_type": "markdown", - "id": "4e044e8a", + "id": "4d2d1040", "metadata": {}, "source": [ "Note that without the `np.ix_` call, only the diagonal elements would be selected:" @@ -3308,7 +3308,7 @@ { "cell_type": "code", "execution_count": 115, - "id": "f13887ba", + "id": "0fab075b", "metadata": {}, "outputs": [ { @@ -3328,7 +3328,7 @@ }, { "cell_type": "markdown", - "id": "cb1e8cf2", + "id": "5470b9dd", "metadata": {}, "source": [ "This difference is the most important thing to remember about indexing with multiple advanced indices.\n", @@ -3349,7 +3349,7 @@ { "cell_type": "code", "execution_count": 116, - "id": "05271311", + "id": "fd9f1a9c", "metadata": {}, "outputs": [ { @@ -3370,7 +3370,7 @@ }, { "cell_type": "markdown", - "id": "ec5b9a35", + "id": "38a15ef6", "metadata": {}, "source": [ "Or wish to add a constant to all negative elements:" @@ -3379,7 +3379,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "291fff1d", + "id": "e6ff83aa", "metadata": {}, "outputs": [ { @@ -3401,7 +3401,7 @@ }, { "cell_type": "markdown", - "id": "034c1975", + "id": "1377eac0", "metadata": {}, "source": [ "In general if an index includes a Boolean array, the result will be identical to inserting `obj.nonzero()` into the same position and using the integer array indexing mechanism described above. `x[ind_1, boolean_array, ind_2]` is equivalent to `x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]`.\n", @@ -3414,7 +3414,7 @@ { "cell_type": "code", "execution_count": 118, - "id": "c58aa8b3", + "id": "46569ba2", "metadata": {}, "outputs": [], "source": [ @@ -3425,7 +3425,7 @@ { "cell_type": "code", "execution_count": 119, - "id": "21849360", + "id": "36b2b148", "metadata": {}, "outputs": [ { @@ -3446,7 +3446,7 @@ { "cell_type": "code", "execution_count": 120, - "id": "e6924d97", + "id": "21f9558e", "metadata": {}, "outputs": [ { @@ -3467,7 +3467,7 @@ }, { "cell_type": "markdown", - "id": "2cbb48f6", + "id": "6eea78ca", "metadata": {}, "source": [ "Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.\n", @@ -3480,7 +3480,7 @@ { "cell_type": "code", "execution_count": 121, - "id": "612560aa", + "id": "deb7f9b0", "metadata": {}, "outputs": [ { @@ -3503,7 +3503,7 @@ }, { "cell_type": "markdown", - "id": "4c8ed0c7", + "id": "f32a04a6", "metadata": {}, "source": [ "Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the `obj.nonzero()` analogy. The function `ix_` also supports boolean arrays and will work without any surprises.\n", @@ -3516,7 +3516,7 @@ { "cell_type": "code", "execution_count": 122, - "id": "c8309bd2", + "id": "00b1054c", "metadata": {}, "outputs": [], "source": [ @@ -3530,7 +3530,7 @@ { "cell_type": "code", "execution_count": 123, - "id": "6f96cb8a", + "id": "9e0b2885", "metadata": {}, "outputs": [ { @@ -3551,7 +3551,7 @@ { "cell_type": "code", "execution_count": 124, - "id": "11741b5c", + "id": "c68d1ca5", "metadata": {}, "outputs": [], "source": [ @@ -3561,7 +3561,7 @@ { "cell_type": "code", "execution_count": 125, - "id": "fd39a729", + "id": "e1b69b0e", "metadata": {}, "outputs": [ { @@ -3582,7 +3582,7 @@ }, { "cell_type": "markdown", - "id": "7ddc6b28", + "id": "5ebd2940", "metadata": {}, "source": [ "Without the n`p.ix_` call, only the diagonal elements would be selected.\n", @@ -3593,7 +3593,7 @@ { "cell_type": "code", "execution_count": 126, - "id": "7ccf9464", + "id": "3f958897", "metadata": {}, "outputs": [ { @@ -3615,7 +3615,7 @@ }, { "cell_type": "markdown", - "id": "564a7af0", + "id": "c30bafa8", "metadata": {}, "source": [ "##### Example 3\n", @@ -3626,7 +3626,7 @@ { "cell_type": "code", "execution_count": 127, - "id": "dce8c929", + "id": "bbd9ffb3", "metadata": {}, "outputs": [ { @@ -3654,7 +3654,7 @@ { "cell_type": "code", "execution_count": 128, - "id": "bcd7a12b", + "id": "123eb6b5", "metadata": {}, "outputs": [ { @@ -3678,7 +3678,7 @@ }, { "cell_type": "markdown", - "id": "a7da394e", + "id": "c941c59b", "metadata": {}, "source": [ "#### Combining advanced and basic indexing\n", @@ -3691,7 +3691,7 @@ { "cell_type": "code", "execution_count": 129, - "id": "dc2df71d", + "id": "4794a87c", "metadata": {}, "outputs": [ { @@ -3714,7 +3714,7 @@ }, { "cell_type": "markdown", - "id": "28f3afe5", + "id": "38d17db1", "metadata": {}, "source": [ "In effect, the slice and index array operation are independent. The slice operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), followed by the index array operation which extracts rows with index 0, 2 and 4 (i.e the first, third and fifth rows). This is equivalent to:" @@ -3723,7 +3723,7 @@ { "cell_type": "code", "execution_count": 130, - "id": "d61f25a8", + "id": "a36fe89e", "metadata": {}, "outputs": [ { @@ -3745,7 +3745,7 @@ }, { "cell_type": "markdown", - "id": "907b0b9d", + "id": "b2e31803", "metadata": {}, "source": [ "A single advanced index can, for example, replace a slice and the result array will be the same. However, it is a copy and may have a different memory layout. A slice is preferable when it is possible. For example:" @@ -3754,7 +3754,7 @@ { "cell_type": "code", "execution_count": 131, - "id": "9a400503", + "id": "9690f141", "metadata": {}, "outputs": [], "source": [ @@ -3767,7 +3767,7 @@ { "cell_type": "code", "execution_count": 132, - "id": "95b3f42f", + "id": "0056a333", "metadata": {}, "outputs": [ { @@ -3788,7 +3788,7 @@ { "cell_type": "code", "execution_count": 133, - "id": "0dbd6221", + "id": "f68d47d7", "metadata": {}, "outputs": [ { @@ -3808,7 +3808,7 @@ }, { "cell_type": "markdown", - "id": "718eb852", + "id": "bd9276eb", "metadata": {}, "source": [ "The easiest way to understand a combination of multiple advanced indices may be to think in terms of the resulting shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:\n", @@ -3834,7 +3834,7 @@ { "cell_type": "code", "execution_count": 134, - "id": "b840bdb4", + "id": "b9a9e721", "metadata": {}, "outputs": [], "source": [ @@ -3845,7 +3845,7 @@ { "cell_type": "code", "execution_count": 135, - "id": "4031f15a", + "id": "d18af53c", "metadata": {}, "outputs": [ { @@ -3870,7 +3870,7 @@ { "cell_type": "code", "execution_count": 136, - "id": "e16ef04d", + "id": "a46c61a7", "metadata": {}, "outputs": [ { @@ -3891,7 +3891,7 @@ }, { "cell_type": "markdown", - "id": "022fb178", + "id": "90461200", "metadata": {}, "source": [ "### Field access\n", @@ -3908,7 +3908,7 @@ { "cell_type": "code", "execution_count": 137, - "id": "dc4dfd32", + "id": "d35521cb", "metadata": {}, "outputs": [], "source": [ @@ -3918,7 +3918,7 @@ { "cell_type": "code", "execution_count": 138, - "id": "437605af", + "id": "4409ef93", "metadata": {}, "outputs": [ { @@ -3939,7 +3939,7 @@ { "cell_type": "code", "execution_count": 139, - "id": "1e9f160b", + "id": "cb258d6f", "metadata": {}, "outputs": [ { @@ -3960,7 +3960,7 @@ { "cell_type": "code", "execution_count": 140, - "id": "e209d12a", + "id": "5edf3085", "metadata": {}, "outputs": [ { @@ -3981,7 +3981,7 @@ { "cell_type": "code", "execution_count": 141, - "id": "8f574141", + "id": "e207b95f", "metadata": {}, "outputs": [ { @@ -4001,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "c1eed605", + "id": "7919d5ec", "metadata": {}, "source": [ "### Flat Iterator indexing\n", @@ -4016,7 +4016,7 @@ { "cell_type": "code", "execution_count": 142, - "id": "5ac958b9", + "id": "26837104", "metadata": {}, "outputs": [], "source": [ @@ -4026,7 +4026,7 @@ }, { "cell_type": "markdown", - "id": "e5fa14f1", + "id": "a6660ba8", "metadata": {}, "source": [ "Or an array of the right size:" @@ -4035,7 +4035,7 @@ { "cell_type": "code", "execution_count": 143, - "id": "03e4fa34", + "id": "db928126", "metadata": {}, "outputs": [], "source": [ @@ -4044,7 +4044,7 @@ }, { "cell_type": "markdown", - "id": "48b8957e", + "id": "1b27d3e5", "metadata": {}, "source": [ "Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints):" @@ -4053,7 +4053,7 @@ { "cell_type": "code", "execution_count": 144, - "id": "78392572", + "id": "e9f98d73", "metadata": {}, "outputs": [ { @@ -4074,7 +4074,7 @@ }, { "cell_type": "markdown", - "id": "2d6aec14", + "id": "67debb07", "metadata": {}, "source": [ "```py\n", @@ -4093,7 +4093,7 @@ { "cell_type": "code", "execution_count": 145, - "id": "817f4e56", + "id": "8e27fdb0", "metadata": {}, "outputs": [ { @@ -4115,7 +4115,7 @@ { "cell_type": "code", "execution_count": 146, - "id": "8d24f864", + "id": "a0c9e4ed", "metadata": {}, "outputs": [ { @@ -4136,7 +4136,7 @@ }, { "cell_type": "markdown", - "id": "fc9b6b02", + "id": "d445872a", "metadata": {}, "source": [ "Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at `x[1] + 1` is assigned to `x[1]` three times, rather than being incremented 3 times.\n", @@ -4149,7 +4149,7 @@ { "cell_type": "code", "execution_count": 147, - "id": "fcf9c20a", + "id": "f2286f20", "metadata": {}, "outputs": [ { @@ -4171,7 +4171,7 @@ }, { "cell_type": "markdown", - "id": "86f6e843", + "id": "dfc6a6e1", "metadata": {}, "source": [ "So one can use code to construct tuples of any number of indices and then use these within an index.\n", @@ -4182,7 +4182,7 @@ { "cell_type": "code", "execution_count": 148, - "id": "13b4d1e0", + "id": "f4effb63", "metadata": {}, "outputs": [ { @@ -4203,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "159319b6", + "id": "21bb89e9", "metadata": {}, "source": [ "Likewise, ellipsis can be specified by code by using the Ellipsis object:" @@ -4212,7 +4212,7 @@ { "cell_type": "code", "execution_count": 149, - "id": "9ca410fa", + "id": "365d65f5", "metadata": {}, "outputs": [ { @@ -4235,7 +4235,7 @@ }, { "cell_type": "markdown", - "id": "767a18dd", + "id": "de8faa4d", "metadata": {}, "source": [ "For this reason, it is possible to use the output from the `np.nonzero()` function directly as an index since it always returns a tuple of index arrays.\n", @@ -4246,7 +4246,7 @@ { "cell_type": "code", "execution_count": 150, - "id": "2580ea87", + "id": "315eda2d", "metadata": {}, "outputs": [ { @@ -4316,7 +4316,7 @@ { "cell_type": "code", "execution_count": 151, - "id": "a9876d42", + "id": "47005c0a", "metadata": {}, "outputs": [ { @@ -4336,7 +4336,7 @@ }, { "cell_type": "markdown", - "id": "e878b8e8", + "id": "090bddf7", "metadata": {}, "source": [ "## Structured arrays\n", @@ -4349,7 +4349,7 @@ { "cell_type": "code", "execution_count": 152, - "id": "4c4c0611", + "id": "63b3f381", "metadata": {}, "outputs": [ { @@ -4372,7 +4372,7 @@ }, { "cell_type": "markdown", - "id": "6df07549", + "id": "d485f421", "metadata": {}, "source": [ "Here `x` is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named `'name'`, 2. a 32-bit integer named `'age'`, and 3. a 32-bit float named `'weight'`.\n", @@ -4383,7 +4383,7 @@ { "cell_type": "code", "execution_count": 153, - "id": "842831f0", + "id": "90aa50c5", "metadata": {}, "outputs": [ { @@ -4403,7 +4403,7 @@ }, { "cell_type": "markdown", - "id": "72ddac9c", + "id": "6dd26439", "metadata": {}, "source": [ "You can access and modify individual fields of a structured array by indexing with the field name:" @@ -4412,7 +4412,7 @@ { "cell_type": "code", "execution_count": 154, - "id": "e34d9aa7", + "id": "92220f87", "metadata": {}, "outputs": [ { @@ -4433,7 +4433,7 @@ { "cell_type": "code", "execution_count": 155, - "id": "126b416f", + "id": "f1d5effa", "metadata": {}, "outputs": [ { @@ -4454,7 +4454,7 @@ { "cell_type": "code", "execution_count": 156, - "id": "912cad13", + "id": "3e347096", "metadata": {}, "outputs": [ { @@ -4475,7 +4475,7 @@ }, { "cell_type": "markdown", - "id": "ca9d77da", + "id": "ef86ad92", "metadata": {}, "source": [ "Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.\n", @@ -4498,7 +4498,7 @@ { "cell_type": "code", "execution_count": 157, - "id": "a651d2ac", + "id": "deaa4b6f", "metadata": {}, "outputs": [ { @@ -4518,7 +4518,7 @@ }, { "cell_type": "markdown", - "id": "85665077", + "id": "e4013774", "metadata": {}, "source": [ "If `fieldname` is the empty string `''`, the field will be given a default name of the form `f#`, where `#` is the integer index of the field, counting from 0 from the left:" @@ -4527,7 +4527,7 @@ { "cell_type": "code", "execution_count": 158, - "id": "5bfb5622", + "id": "290e5a71", "metadata": {}, "outputs": [ { @@ -4547,7 +4547,7 @@ }, { "cell_type": "markdown", - "id": "7d7bda10", + "id": "9c79739f", "metadata": {}, "source": [ "The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.\n", @@ -4560,7 +4560,7 @@ { "cell_type": "code", "execution_count": 159, - "id": "dbfc5653", + "id": "1785e5e8", "metadata": {}, "outputs": [ { @@ -4581,7 +4581,7 @@ { "cell_type": "code", "execution_count": 160, - "id": "9215972d", + "id": "b21eec02", "metadata": {}, "outputs": [ { @@ -4601,7 +4601,7 @@ }, { "cell_type": "markdown", - "id": "f22664b6", + "id": "5b908d92", "metadata": {}, "source": [ "- A dictionary of field parameter arrays\n", @@ -4614,7 +4614,7 @@ { "cell_type": "code", "execution_count": 161, - "id": "584c9e57", + "id": "d39c3007", "metadata": {}, "outputs": [ { @@ -4635,7 +4635,7 @@ { "cell_type": "code", "execution_count": 162, - "id": "96debf43", + "id": "e9ee8fa1", "metadata": {}, "outputs": [ { @@ -4658,7 +4658,7 @@ }, { "cell_type": "markdown", - "id": "87a73b3e", + "id": "4b88bdf7", "metadata": {}, "source": [ "Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of `numpy.object_` type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it.\n", @@ -4673,7 +4673,7 @@ { "cell_type": "code", "execution_count": 163, - "id": "bb4ad3d2", + "id": "d635ab63", "metadata": {}, "outputs": [ { @@ -4693,7 +4693,7 @@ }, { "cell_type": "markdown", - "id": "394a0bce", + "id": "b14afd80", "metadata": {}, "source": [ "#### Manipulating and Displaying Structured Datatypes\n", @@ -4704,7 +4704,7 @@ { "cell_type": "code", "execution_count": 164, - "id": "8da55622", + "id": "0ef3750a", "metadata": {}, "outputs": [ { @@ -4725,7 +4725,7 @@ }, { "cell_type": "markdown", - "id": "ef61254c", + "id": "558d5321", "metadata": {}, "source": [ "The field names may be modified by assigning to the `names` attribute using a sequence of strings of the same length.\n", @@ -4736,7 +4736,7 @@ { "cell_type": "code", "execution_count": 165, - "id": "35e677f7", + "id": "7b0f8b3c", "metadata": {}, "outputs": [ { @@ -4756,7 +4756,7 @@ }, { "cell_type": "markdown", - "id": "5b0965e6", + "id": "a9b44ff7", "metadata": {}, "source": [ "Both the `names` and `fields` attributes will equal `None` for unstructured arrays. The recommended way to test if a dtype is structured is with `if dt.names is not None` rather than `if dt.names`, to account for dtypes with 0 fields.\n", @@ -4773,7 +4773,7 @@ { "cell_type": "code", "execution_count": 166, - "id": "808b8e57", + "id": "9a4995a3", "metadata": {}, "outputs": [ { @@ -4793,7 +4793,7 @@ }, { "cell_type": "markdown", - "id": "102ab5a9", + "id": "dc735227", "metadata": {}, "source": [ "When using the first form of dictionary-based specification, the titles may be supplied as an extra `'titles'` key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple `(datatype, offset, title)` instead of the usual 2-element tuple:" @@ -4802,7 +4802,7 @@ { "cell_type": "code", "execution_count": 167, - "id": "5e2bdf5e", + "id": "f0e75f91", "metadata": {}, "outputs": [ { @@ -4822,7 +4822,7 @@ }, { "cell_type": "markdown", - "id": "5dc79104", + "id": "67b0c4ea", "metadata": {}, "source": [ "The `dtype.fields` dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the `names` attribute preserves the field order while the `fields` attribute may not, it is recommended to iterate through the fields of a dtype using the `names` attribute of the dtype, which will not list titles, as in:" @@ -4831,7 +4831,7 @@ { "cell_type": "code", "execution_count": 168, - "id": "8a797059", + "id": "3456dc79", "metadata": {}, "outputs": [ { @@ -4850,7 +4850,7 @@ }, { "cell_type": "markdown", - "id": "16de7203", + "id": "afd441be", "metadata": {}, "source": [ "### Indexing and Assignment to Structured arrays\n", @@ -4867,7 +4867,7 @@ { "cell_type": "code", "execution_count": 169, - "id": "b3c0653e", + "id": "38586c54", "metadata": {}, "outputs": [ { @@ -4890,7 +4890,7 @@ }, { "cell_type": "markdown", - "id": "e3161555", + "id": "93d6a23e", "metadata": {}, "source": [ "##### Assignment from Scalars\n", @@ -4901,7 +4901,7 @@ { "cell_type": "code", "execution_count": 170, - "id": "3a8d4e40", + "id": "6360f54c", "metadata": {}, "outputs": [], "source": [ @@ -4911,7 +4911,7 @@ { "cell_type": "code", "execution_count": 171, - "id": "48873091", + "id": "302c9869", "metadata": {}, "outputs": [ { @@ -4934,7 +4934,7 @@ { "cell_type": "code", "execution_count": 172, - "id": "0f6129f7", + "id": "974623bd", "metadata": {}, "outputs": [ { @@ -4956,7 +4956,7 @@ }, { "cell_type": "markdown", - "id": "2e114197", + "id": "58967ba4", "metadata": {}, "source": [ "Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field:" @@ -4965,7 +4965,7 @@ { "cell_type": "code", "execution_count": 173, - "id": "1e817a2d", + "id": "e76df064", "metadata": {}, "outputs": [], "source": [ @@ -4976,7 +4976,7 @@ { "cell_type": "code", "execution_count": 174, - "id": "b276510e", + "id": "83e60957", "metadata": {}, "outputs": [], "source": [ @@ -4985,7 +4985,7 @@ }, { "cell_type": "markdown", - "id": "d6cb1be7", + "id": "e6cc9b94", "metadata": {}, "source": [ "```py\n", @@ -5006,7 +5006,7 @@ { "cell_type": "code", "execution_count": 175, - "id": "86c62c21", + "id": "6ab587e4", "metadata": {}, "outputs": [], "source": [ @@ -5017,7 +5017,7 @@ { "cell_type": "code", "execution_count": 176, - "id": "c00582b0", + "id": "bef52248", "metadata": {}, "outputs": [ { @@ -5039,7 +5039,7 @@ }, { "cell_type": "markdown", - "id": "66dbb2d7", + "id": "b284d616", "metadata": {}, "source": [ "##### Assignment involving subarrays\n", @@ -5056,7 +5056,7 @@ { "cell_type": "code", "execution_count": 177, - "id": "7ab77ab8", + "id": "2d9bd35c", "metadata": {}, "outputs": [ { @@ -5078,7 +5078,7 @@ { "cell_type": "code", "execution_count": 178, - "id": "1512b7c1", + "id": "62c3b75c", "metadata": {}, "outputs": [ { @@ -5099,7 +5099,7 @@ }, { "cell_type": "markdown", - "id": "1f9b14bd", + "id": "254c5b6f", "metadata": {}, "source": [ "The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array." @@ -5108,7 +5108,7 @@ { "cell_type": "code", "execution_count": 179, - "id": "7408fddd", + "id": "204a8591", "metadata": {}, "outputs": [ { @@ -5130,7 +5130,7 @@ }, { "cell_type": "markdown", - "id": "5857f0c1", + "id": "2d3f5eb5", "metadata": {}, "source": [ "This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures." @@ -5139,7 +5139,7 @@ { "cell_type": "code", "execution_count": 180, - "id": "5aa16f19", + "id": "ebc639ef", "metadata": {}, "outputs": [ { @@ -5159,7 +5159,7 @@ }, { "cell_type": "markdown", - "id": "3e10749c", + "id": "c69aa084", "metadata": {}, "source": [ "If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result:" @@ -5168,7 +5168,7 @@ { "cell_type": "code", "execution_count": 181, - "id": "62e622e5", + "id": "c00e0059", "metadata": {}, "outputs": [], "source": [ @@ -5178,7 +5178,7 @@ { "cell_type": "code", "execution_count": 182, - "id": "7a7ff9ff", + "id": "50654920", "metadata": {}, "outputs": [ { @@ -5199,7 +5199,7 @@ { "cell_type": "code", "execution_count": 183, - "id": "c81fc737", + "id": "9e250a2b", "metadata": {}, "outputs": [ { @@ -5219,7 +5219,7 @@ }, { "cell_type": "markdown", - "id": "0c7fbe27", + "id": "320450d5", "metadata": {}, "source": [ "##### Accessing multiple fields\n", @@ -5232,7 +5232,7 @@ { "cell_type": "code", "execution_count": 184, - "id": "ae1f0721", + "id": "38283ceb", "metadata": {}, "outputs": [ { @@ -5254,7 +5254,7 @@ }, { "cell_type": "markdown", - "id": "dc1fdde2", + "id": "7394672a", "metadata": {}, "source": [ "Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.\n", @@ -5265,7 +5265,7 @@ { "cell_type": "code", "execution_count": 185, - "id": "85fe6386", + "id": "8e5e8715", "metadata": {}, "outputs": [ { @@ -5287,7 +5287,7 @@ }, { "cell_type": "markdown", - "id": "83513e05", + "id": "087b4865", "metadata": {}, "source": [ "This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes:" @@ -5296,7 +5296,7 @@ { "cell_type": "code", "execution_count": 186, - "id": "d6698eb0", + "id": "4bc94f52", "metadata": {}, "outputs": [], "source": [ @@ -5305,7 +5305,7 @@ }, { "cell_type": "markdown", - "id": "cd8e9380", + "id": "15b0fb17", "metadata": {}, "source": [ "##### Indexing with an integer to get a structured scalar\n", @@ -5316,7 +5316,7 @@ { "cell_type": "code", "execution_count": 187, - "id": "dd10ae37", + "id": "c3d7971d", "metadata": {}, "outputs": [ { @@ -5339,7 +5339,7 @@ { "cell_type": "code", "execution_count": 188, - "id": "2dd9edfd", + "id": "98c6761c", "metadata": {}, "outputs": [ { @@ -5359,7 +5359,7 @@ }, { "cell_type": "markdown", - "id": "7021926f", + "id": "fe0a55b0", "metadata": {}, "source": [ "Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name:" @@ -5368,7 +5368,7 @@ { "cell_type": "code", "execution_count": 189, - "id": "38025152", + "id": "c5548f9d", "metadata": {}, "outputs": [ { @@ -5391,7 +5391,7 @@ }, { "cell_type": "markdown", - "id": "267858ca", + "id": "a69274e5", "metadata": {}, "source": [ "Similarly to tuples, structured scalars can also be indexed with an integer:" @@ -5400,7 +5400,7 @@ { "cell_type": "code", "execution_count": 190, - "id": "df9a2bb5", + "id": "b071db04", "metadata": {}, "outputs": [ { @@ -5422,7 +5422,7 @@ { "cell_type": "code", "execution_count": 191, - "id": "a4d18340", + "id": "d6cf56dd", "metadata": {}, "outputs": [], "source": [ @@ -5431,7 +5431,7 @@ }, { "cell_type": "markdown", - "id": "c04f3aa0", + "id": "72236f47", "metadata": {}, "source": [ "Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`:" @@ -5440,7 +5440,7 @@ { "cell_type": "code", "execution_count": 192, - "id": "44d4f5b2", + "id": "7a94231e", "metadata": {}, "outputs": [ { @@ -5460,7 +5460,7 @@ }, { "cell_type": "markdown", - "id": "f30dc4b7", + "id": "97d3b3c8", "metadata": {}, "source": [ "#### Viewing structured arrays containing objects\n", @@ -5475,7 +5475,7 @@ { "cell_type": "code", "execution_count": 193, - "id": "4fd31f79", + "id": "8096d806", "metadata": {}, "outputs": [ { @@ -5497,7 +5497,7 @@ }, { "cell_type": "markdown", - "id": "424d1ba7", + "id": "fe929ae9", "metadata": {}, "source": [ "NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the `'f4'` dtype for the `'a'` field):" @@ -5506,7 +5506,7 @@ { "cell_type": "code", "execution_count": 194, - "id": "baaa67b7", + "id": "90707423", "metadata": {}, "outputs": [ { @@ -5527,7 +5527,7 @@ }, { "cell_type": "markdown", - "id": "80b3e916", + "id": "a0b2b907", "metadata": {}, "source": [ "To compare two structured arrays, it must be possible to promote them to a common dtype as returned by `numpy.result_type` and `np.promote_types`. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields:" @@ -5536,7 +5536,7 @@ { "cell_type": "code", "execution_count": 195, - "id": "84e4108a", + "id": "e0c738b6", "metadata": {}, "outputs": [ { @@ -5557,7 +5557,7 @@ { "cell_type": "code", "execution_count": 196, - "id": "c3f17d88", + "id": "5c326de2", "metadata": {}, "outputs": [ { @@ -5577,7 +5577,7 @@ }, { "cell_type": "markdown", - "id": "cb87b09c", + "id": "2a07d070", "metadata": {}, "source": [ "The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed:" @@ -5586,7 +5586,7 @@ { "cell_type": "code", "execution_count": 197, - "id": "20a61efb", + "id": "cc5390b9", "metadata": {}, "outputs": [ { @@ -5608,7 +5608,7 @@ { "cell_type": "code", "execution_count": 198, - "id": "cf3b4857", + "id": "dc517995", "metadata": {}, "outputs": [ { @@ -5628,7 +5628,7 @@ }, { "cell_type": "markdown", - "id": "9f4e3590", + "id": "abad5718", "metadata": {}, "source": [ "Note that the result prints without `offsets` or `itemsize` indicating no additional padding. If a structured dtype is created with `align=True` ensuring that `dtype.isalignedstruct` is true, this property is preserved:" @@ -5637,7 +5637,7 @@ { "cell_type": "code", "execution_count": 199, - "id": "6363970b", + "id": "3e506071", "metadata": {}, "outputs": [ { @@ -5659,7 +5659,7 @@ { "cell_type": "code", "execution_count": 200, - "id": "e000dc13", + "id": "ebd6df4b", "metadata": {}, "outputs": [ { @@ -5680,7 +5680,7 @@ { "cell_type": "code", "execution_count": 201, - "id": "b47c2d91", + "id": "bc3de171", "metadata": {}, "outputs": [ { @@ -5700,7 +5700,7 @@ }, { "cell_type": "markdown", - "id": "fad22d96", + "id": "634fd8e1", "metadata": {}, "source": [ "When promoting multiple dtypes, the result is aligned if any of the inputs is:" @@ -5709,7 +5709,7 @@ { "cell_type": "code", "execution_count": 202, - "id": "12393ade", + "id": "39180642", "metadata": {}, "outputs": [ { @@ -5729,7 +5729,7 @@ }, { "cell_type": "markdown", - "id": "4a7da9ec", + "id": "7ac9811b", "metadata": {}, "source": [ "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n", diff --git a/assignments/data-science/analyzing-COVID-19-papers.html b/assignments/data-science/analyzing-COVID-19-papers.html index eb891ff99b..a4fb7d64eb 100644 --- a/assignments/data-science/analyzing-COVID-19-papers.html +++ b/assignments/data-science/analyzing-COVID-19-papers.html @@ -1697,7 +1697,9 @@

    40.22.1. Getting the data
    -
    diff --git a/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html b/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html index 578fbdf460..640e3c68f6 100644 --- a/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html +++ b/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html @@ -1764,8 +1764,7 @@

    Contents

    -

    -
    +

    40.95. Intro to TensorFlow for Deep Learning#

    40.95.1. 1. What is TensorFlow#

    diff --git a/assignments/machine-learning-productionization/debugging-in-classification.html b/assignments/machine-learning-productionization/debugging-in-classification.html index faf586dc37..316f2714c8 100644 --- a/assignments/machine-learning-productionization/debugging-in-classification.html +++ b/assignments/machine-learning-productionization/debugging-in-classification.html @@ -1845,7 +1845,7 @@

    40.84.1. Load MNIST Data# Load data mnistDf_backup = pd.read_csv( - "https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv", + "https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/mnist_train_small.csv", sep=",", header=None) # Shuffle data diff --git a/assignments/machine-learning-productionization/debugging-in-regression.html b/assignments/machine-learning-productionization/debugging-in-regression.html index 1729b639ed..9020c6e4d1 100644 --- a/assignments/machine-learning-productionization/debugging-in-regression.html +++ b/assignments/machine-learning-productionization/debugging-in-regression.html @@ -1907,7 +1907,7 @@

    40.85.1. Setuppd.options.display.float_format = '{:.1f}'.format wineDf = pd.read_csv( - "https://download.mlcc.google.com/mledu-datasets/winequality.csv", + "https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/winequality.csv", encoding='latin-1') wineDf.columns = ['fixed acidity','volatile acidity','citric acid', 'residual sugar','chlorides','free sulfur dioxide', diff --git a/data-science/data-visualization/visualization-relationships.html b/data-science/data-visualization/visualization-relationships.html index 8b28f6742e..bd0372b3ee 100644 --- a/data-science/data-visualization/visualization-relationships.html +++ b/data-science/data-visualization/visualization-relationships.html @@ -2029,7 +2029,7 @@

    6.3.3. Facet grids

    -
    <seaborn.axisgrid.FacetGrid at 0x7fe404455940>
    +
    <seaborn.axisgrid.FacetGrid at 0x7ff0c05b58b0>
     
    ../../_images/visualization-relationships_18_1.png diff --git a/data-science/introduction/introduction-to-statistics-and-probability.html b/data-science/introduction/introduction-to-statistics-and-probability.html index d8eff0f8cc..8e7d83f702 100644 --- a/data-science/introduction/introduction-to-statistics-and-probability.html +++ b/data-science/introduction/introduction-to-statistics-and-probability.html @@ -1912,10 +1912,10 @@

    4.4.6. Normal distribution -
    -
    <memory at 0x7f806c85fd40>
    +
    <memory at 0x7f12d2318d40>
     
    diff --git a/data-science/working-with-data/pandas/data-selection.html b/data-science/working-with-data/pandas/data-selection.html index bd10b561c9..cd6dc9bc74 100644 --- a/data-science/working-with-data/pandas/data-selection.html +++ b/data-science/working-with-data/pandas/data-selection.html @@ -1834,24 +1834,24 @@

    5.4.2.2. Selection by label5.4.2.2. Selection by label -
    c    0.038589
    -d   -0.197195
    -e   -0.130250
    -f    0.067106
    +
    c    1.284624
    +d   -0.508137
    +e    1.510474
    +f    0.757249
     dtype: float64
     
    @@ -1978,7 +1978,7 @@

    5.4.2.2. Selection by label -
    -0.9518306057687721
    +
    1.64675837211757
     
    @@ -2035,8 +2035,8 @@

    5.4.2.2. Selection by label -
    a    0.004982
    -b   -0.951831
    +
    a   -0.579541
    +b    1.646758
     c    0.000000
     d    0.000000
     e    0.000000
    @@ -2123,24 +2123,24 @@ 

    5.4.2.2. Selection by label5.4.2.2. Selection by label5.4.2.2. Selection by label -
    A    1.699244
    -B   -1.280140
    -C    0.683763
    -D   -0.078884
    +
    A   -0.261255
    +B    0.563448
    +C   -0.587615
    +D    0.125141
     Name: a, dtype: float64
     
    @@ -2307,10 +2307,10 @@

    5.4.2.2. Selection by label -
    A     True
    -B    False
    -C     True
    -D    False
    +
    A    False
    +B     True
    +C    False
    +D     True
     Name: a, dtype: bool
     
    @@ -2380,40 +2380,40 @@

    5.4.2.2. Selection by label - A - C + B + D a - 1.699244 - 0.683763 + 0.563448 + 0.125141 b - 0.210853 - -0.302834 + -1.116184 + 1.314941 c - -2.148546 - -0.813316 + 1.297326 + -0.802559 d - -0.639539 - 1.754627 + -2.411222 + 0.860725 e - 2.519738 - 0.338291 + -1.138838 + 0.100947 f - 1.627156 - -0.872900 + -0.202684 + 0.818771 @@ -2512,17 +2512,17 @@

    5.4.2.2. Selection by label5.4.2.2. Selection by label -
    1.699243786401637
    +
    -0.2612545763775011
     
    @@ -2903,9 +2903,9 @@

    5.4.2.4. Selection by position -
    0    0.608692
    -2    1.039676
    -4    0.704550
    +
    0    0.547894
    +2   -1.356373
    +4    2.255724
     dtype: float64
     
    @@ -2957,7 +2957,7 @@

    5.4.2.4. Selection by position - @@ -3098,24 +3098,24 @@

    5.4.2.4. Selection by position5.4.2.4. Selection by position5.4.2.4. Selection by position5.4.2.4. Selection by position5.4.2.4. Selection by position5.4.2.4. Selection by position -
    1.495843419947373
    +
    2.9734688264008327
     
    @@ -3558,10 +3558,10 @@

    5.4.2.4. Selection by position -
    0    0.336735
    -2    1.495843
    -4   -0.695248
    -6    0.940683
    +
    0    1.270549
    +2    2.973469
    +4    3.005324
    +6    2.231575
     Name: 2, dtype: float64
     
    @@ -3853,23 +3853,23 @@

    5.4.2.4. Selection by position5.4.2.4. Selection by position5.4.2.5. Selection by callable5.4.2.5. Selection by callable5.4.2.5. Selection by callable5.4.2.5. Selection by callable -
    a   -0.248610
    -b    0.920658
    -c    0.945481
    -d    0.259017
    -e    2.184927
    -f    0.153795
    +
    a    1.026795
    +b    1.602264
    +c    0.648559
    +d   -1.229180
    +e    0.171712
    +f   -0.419989
     Name: A, dtype: float64
     
    @@ -4518,11 +4511,10 @@

    5.4.2.5. Selection by callable -
    b    0.920658
    -c    0.945481
    -d    0.259017
    -e    2.184927
    -f    0.153795
    +
    a    1.026795
    +b    1.602264
    +c    0.648559
    +e    0.171712
     Name: A, dtype: float64
     
    diff --git a/data-science/working-with-data/pandas/introduction-and-data-structures.html b/data-science/working-with-data/pandas/introduction-and-data-structures.html index 4e0909872b..cef6c56810 100644 --- a/data-science/working-with-data/pandas/introduction-and-data-structures.html +++ b/data-science/working-with-data/pandas/introduction-and-data-structures.html @@ -2011,11 +2011,11 @@
    5.4.1.2.1.1.1. From ndarray -
    a   -0.471148
    -b   -0.093864
    -c   -0.240233
    -d    1.084377
    -e    0.740195
    +
    a   -0.473693
    +b   -2.010741
    +c    0.446526
    +d    0.111314
    +e   -1.301965
     dtype: float64
     
    @@ -2040,11 +2040,11 @@
    5.4.1.2.1.1.1. From ndarray -
    -
    -
    -
    -
    -
    a    0.624285
    -b    0.910406
    -c    0.786445
    -d    2.957596
    -e    2.096344
    +
    a    0.622698
    +b    0.133889
    +c    1.562874
    +d    1.117746
    +e    0.271997
     dtype: float64
     
    @@ -2244,8 +2244,8 @@

    5.4.1.2.1.2. Series is ndarray-like
    -
    -
    -
    a    -0.471148
    -b    -0.093864
    -c    -0.240233
    -d     1.084377
    +
    a    -0.473693
    +b    -2.010741
    +c     0.446526
    +d     0.111314
     e    12.000000
     dtype: float64
     
    @@ -2422,10 +2422,10 @@

    5.4.1.2.1.4. Vectorized operations and l

    -
    a    -0.942296
    -b    -0.187729
    -c    -0.480465
    -d     2.168753
    +
    a    -0.947387
    +b    -4.021481
    +c     0.893053
    +d     0.222629
     e    24.000000
     dtype: float64
     
    @@ -2439,10 +2439,10 @@

    5.4.1.2.1.4. Vectorized operations and l

    -
    a    -0.942296
    -b    -0.187729
    -c    -0.480465
    -d     2.168753
    +
    a    -0.947387
    +b    -4.021481
    +c     0.893053
    +d     0.222629
     e    24.000000
     dtype: float64
     
    @@ -2456,10 +2456,10 @@

    5.4.1.2.1.4. Vectorized operations and l

    -
    a         0.624285
    -b         0.910406
    -c         0.786445
    -d         2.957596
    +
    a         0.622698
    +b         0.133889
    +c         1.562874
    +d         1.117746
     e    162754.791419
     dtype: float64
     
    @@ -2475,9 +2475,9 @@

    5.4.1.2.1.4. Vectorized operations and l

    a         NaN
    -b   -0.187729
    -c   -0.480465
    -d    2.168753
    +b   -4.021481
    +c    0.893053
    +d    0.222629
     e         NaN
     dtype: float64
     
    @@ -2507,11 +2507,11 @@

    5.4.1.2.1.5. Name attribute -
    0    0.857799
    -1    0.804311
    -2    0.105946
    -3    1.011575
    -4    0.002346
    +
    0   -1.062093
    +1   -0.909160
    +2   -0.156057
    +3   -1.000891
    +4    0.789003
     Name: something, dtype: float64
     
    @@ -4523,51 +4523,51 @@

    5.4.1.2.2.5. Data alignment and arithmet 0 - -2.976357 - -1.316627 - 0.672159 + -0.065012 + 2.049261 + -1.751186 NaN 1 - 0.477964 - 0.339977 - 1.442367 + 0.237794 + -1.335222 + -1.459176 NaN 2 - 0.554947 - -0.146307 - 1.304767 + -0.350036 + -0.347083 + -1.255875 NaN 3 - 0.947704 - 0.868173 - 2.125977 + 1.327497 + -0.523681 + 0.255007 NaN 4 - 1.597975 - -0.998993 - -1.187223 + 2.552969 + 0.157565 + 0.546745 NaN 5 - -1.066379 - 1.095593 - -0.988821 + -1.351615 + -0.333879 + -0.272417 NaN 6 - 0.067644 - -0.587502 - 1.405716 + 0.825050 + -0.179525 + -2.036190 NaN @@ -4637,66 +4637,66 @@

    5.4.1.2.2.5. Data alignment and arithmet 1 - 1.884459 - -0.153521 - 0.043441 - -0.381469 + 0.209956 + -1.067719 + 0.171564 + -0.940577 2 - 3.101853 - 0.780966 - 1.009441 - -3.495847 + -0.058460 + -1.393320 + -0.737118 + -4.419007 3 - 1.830482 - 0.956365 - -0.295293 - -0.735077 + 1.059072 + -1.374886 + 2.448379 + -1.033772 4 - 2.716588 - 1.019025 - -1.171295 - 0.352544 + 1.760605 + -0.961366 + 0.416068 + -3.815386 5 - 1.565483 - 2.371978 - -1.698923 - -1.818532 + -0.295168 + -1.773900 + 0.350974 + 0.619974 6 - 0.744948 - -0.013414 - -0.221765 - -1.769200 + 0.910916 + -2.385331 + 0.800347 + -1.478140 7 - 0.312613 - -0.304193 - 0.816620 - -1.095031 + 0.557560 + -0.757185 + 0.558972 + -2.264130 8 - 1.756536 - 0.536441 - 1.088142 - -0.657246 + 1.292969 + -0.249907 + 0.502733 + -0.763507 9 - 2.103343 - -0.251893 - -2.375203 - -1.608417 + -0.515080 + 0.039775 + 1.113023 + -1.913248 @@ -4737,73 +4737,73 @@

    5.4.1.2.2.5. Data alignment and arithmet 0 - -6.492551 - -0.114228 - 5.049975 - 4.893690 + 1.447580 + 8.755727 + -1.095820 + 11.971909 1 - 2.929745 - -0.881832 - 5.267180 - 2.986346 + 2.497361 + 3.417134 + -0.238001 + 7.269023 2 - 9.016714 - 3.790604 - 10.097182 - -12.585544 + 1.155280 + 1.789129 + -4.781408 + -10.123126 3 - 2.659859 - 4.667597 - 3.573509 - 1.218305 + 6.742939 + 1.881295 + 11.146076 + 6.803049 4 - 7.090390 - 4.980895 - -0.806500 - 6.656411 + 10.250605 + 3.948896 + 0.984520 + -7.105023 5 - 1.334864 - 11.745660 - -3.444640 - -4.198972 + -0.028263 + -0.113775 + 0.659050 + 15.071779 6 - -2.767812 - -0.181299 - 3.941152 - -3.952308 + 6.002159 + -3.170930 + 2.905917 + 4.581208 7 - -4.929484 - -1.635194 - 9.133073 - -0.581465 + 4.235380 + 4.969804 + 1.699039 + 0.651259 8 - 2.290130 - 2.567976 - 10.490684 - 1.607458 + 7.912423 + 7.506190 + 1.417844 + 8.154376 9 - 4.024164 - -1.373693 - -6.826041 - -3.148394 + -1.127819 + 8.954603 + 4.469294 + 2.405671 @@ -4843,73 +4843,73 @@

    5.4.1.2.2.5. Data alignment and arithmet 0 - -0.588751 - -2.364930 - 1.639358 - 1.727897 + -9.051079 + 0.740113 + -1.615081 + 0.501409 1 - 5.377818 - -1.735008 - 1.530372 - 5.069215 + 10.053063 + 3.528249 + -2.234136 + 0.948943 2 - 0.712584 - 2.792353 - 0.617499 - -0.342805 + -5.919119 + -23.711214 + -0.737310 + -0.412435 3 - 7.577377 - 1.874346 - 3.177611 - -6.396355 + 1.054199 + -42.121185 + 0.546683 + 1.041005 4 - 0.982243 - 1.677348 - -1.781578 - 1.073788 + 0.606016 + 2.565555 + -4.923781 + -0.549147 5 - -7.517254 - 0.513049 - -0.918334 - -0.806585 + -2.465164 + -2.365436 + -3.728700 + 0.382503 6 - -1.048699 - -2.292212 - 2.575790 - -0.840010 + 1.249326 + -0.966944 + 5.519267 + 1.937077 7 - -0.721554 - -1.375442 - 0.700960 - -1.936885 + 2.236756 + 1.683613 + -16.613452 + -3.707162 8 - 17.233678 - 8.803186 - 0.588881 - -12.737492 + 0.845677 + 0.908069 + -8.588758 + 0.812430 9 - 2.470156 - -1.482055 - -0.566505 - -0.971177 + -1.598558 + 0.718948 + 2.024870 + 12.325266 @@ -4949,73 +4949,73 @@

    5.4.1.2.2.5. Data alignment and arithmet 0 - 8.322860 - 0.031969 - 0.138454 - 0.112183 + 0.000149 + 3.332793e+00 + 0.146968 + 15.820974 1 - 0.001196 - 0.110356 - 0.182311 - 0.001514 + 0.000098 + 6.453023e-03 + 0.040139 + 1.233219 2 - 3.878422 - 0.016448 - 6.877897 - 72.411995 + 0.000815 + 3.163624e-06 + 3.383760 + 34.560378 3 - 0.000303 - 0.081022 - 0.009808 - 0.000597 + 0.809674 + 3.176857e-07 + 11.195903 + 0.851507 4 - 1.074297 - 0.126330 - 0.099261 - 0.752186 + 7.414182 + 2.308205e-02 + 0.001701 + 10.996238 5 - 0.000313 - 14.433281 - 1.406037 - 2.362645 + 0.027078 + 3.194147e-02 + 0.005173 + 46.715265 6 - 0.826793 - 0.036223 - 0.022717 - 2.008453 + 0.410485 + 1.143917e+00 + 0.001078 + 0.071025 7 - 3.689128 - 0.279403 - 4.142158 - 0.071053 + 0.039951 + 1.244604e-01 + 0.000013 + 0.005295 8 - 0.000011 - 0.000167 - 8.315544 - 0.000038 + 1.955158 + 1.470702e+00 + 0.000184 + 2.295388 9 - 0.026860 - 0.207273 - 9.709205 - 1.124105 + 0.153139 + 3.742910e+00 + 0.059486 + 0.000043 @@ -5271,35 +5271,35 @@

    5.4.1.2.2.6. Transposing5.4.1.3.2. Basics 2000-01-01 - 0.801076 - 1.465342 - -0.166177 - -0.788715 + 0.266217 + 1.228785 + 1.031277 + -0.095949 2000-01-02 - 0.348102 - -1.058147 - -1.531875 - -1.316167 + 1.488999 + 0.037730 + -0.392898 + 0.215211 2000-01-03 - 1.736169 - -0.839949 - 1.481805 - -0.264777 + 0.038978 + -2.140597 + 0.568707 + 1.023702 2000-01-04 - -0.435348 - 0.694244 - 1.922322 - -0.349232 + -0.079610 + -0.033805 + 0.101702 + 0.303200 2000-01-05 - -0.745863 - -0.692943 - -0.828683 - 2.427089 + -2.045461 + -0.369304 + -0.452429 + 2.246294 2000-01-06 - 0.738183 - 0.224374 - -0.953017 - -0.067219 + 0.301222 + -0.225712 + 1.916028 + 0.982501 2000-01-07 - 0.387631 - -0.026705 - -0.647102 - -0.489077 + -0.566633 + 0.042583 + -2.632710 + 0.073967 2000-01-08 - -1.247095 - -0.649027 - -0.993315 - 0.407961 + -0.859050 + -0.604785 + -1.571164 + -1.762992 @@ -5494,7 +5494,7 @@

    5.4.1.3.2. Basics

    -
    0.7381831630668674
    +
    0.30122197945169477
     
    @@ -5534,59 +5534,59 @@

    5.4.1.3.2. Basics 2000-01-01 - 0.801076 - 1.465342 - -0.166177 - -0.788715 + 0.266217 + 1.228785 + 1.031277 + -0.095949 2000-01-02 - 0.348102 - -1.058147 - -1.531875 - -1.316167 + 1.488999 + 0.037730 + -0.392898 + 0.215211 2000-01-03 - 1.736169 - -0.839949 - 1.481805 - -0.264777 + 0.038978 + -2.140597 + 0.568707 + 1.023702 2000-01-04 - -0.435348 - 0.694244 - 1.922322 - -0.349232 + -0.079610 + -0.033805 + 0.101702 + 0.303200 2000-01-05 - -0.745863 - -0.692943 - -0.828683 - 2.427089 + -2.045461 + -0.369304 + -0.452429 + 2.246294 2000-01-06 - 0.738183 - 0.224374 - -0.953017 - -0.067219 + 0.301222 + -0.225712 + 1.916028 + 0.982501 2000-01-07 - 0.387631 - -0.026705 - -0.647102 - -0.489077 + -0.566633 + 0.042583 + -2.632710 + 0.073967 2000-01-08 - -1.247095 - -0.649027 - -0.993315 - 0.407961 + -0.859050 + -0.604785 + -1.571164 + -1.762992 @@ -5627,59 +5627,59 @@

    5.4.1.3.2. Basics 2000-01-01 - 1.465342 - 0.801076 - -0.166177 - -0.788715 + 1.228785 + 0.266217 + 1.031277 + -0.095949 2000-01-02 - -1.058147 - 0.348102 - -1.531875 - -1.316167 + 0.037730 + 1.488999 + -0.392898 + 0.215211 2000-01-03 - -0.839949 - 1.736169 - 1.481805 - -0.264777 + -2.140597 + 0.038978 + 0.568707 + 1.023702 2000-01-04 - 0.694244 - -0.435348 - 1.922322 - -0.349232 + -0.033805 + -0.079610 + 0.101702 + 0.303200 2000-01-05 - -0.692943 - -0.745863 - -0.828683 - 2.427089 + -0.369304 + -2.045461 + -0.452429 + 2.246294 2000-01-06 - 0.224374 - 0.738183 - -0.953017 - -0.067219 + -0.225712 + 0.301222 + 1.916028 + 0.982501 2000-01-07 - -0.026705 - 0.387631 - -0.647102 - -0.489077 + 0.042583 + -0.566633 + -2.632710 + 0.073967 2000-01-08 - -0.649027 - -1.247095 - -0.993315 - 0.407961 + -0.604785 + -0.859050 + -1.571164 + -1.762992 @@ -5720,43 +5720,43 @@

    5.4.1.3.2. Basics 2000-01-01 - 1.465342 - 0.801076 + 1.228785 + 0.266217 2000-01-02 - -1.058147 - 0.348102 + 0.037730 + 1.488999 2000-01-03 - -0.839949 - 1.736169 + -2.140597 + 0.038978 2000-01-04 - 0.694244 - -0.435348 + -0.033805 + -0.079610 2000-01-05 - -0.692943 - -0.745863 + -0.369304 + -2.045461 2000-01-06 - 0.224374 - 0.738183 + -0.225712 + 0.301222 2000-01-07 - -0.026705 - 0.387631 + 0.042583 + -0.566633 2000-01-08 - -0.649027 - -1.247095 + -0.604785 + -0.859050 @@ -5795,43 +5795,43 @@

    5.4.1.3.2. Basics 2000-01-01 - 1.465342 - 0.801076 + 1.228785 + 0.266217 2000-01-02 - -1.058147 - 0.348102 + 0.037730 + 1.488999 2000-01-03 - -0.839949 - 1.736169 + -2.140597 + 0.038978 2000-01-04 - 0.694244 - -0.435348 + -0.033805 + -0.079610 2000-01-05 - -0.692943 - -0.745863 + -0.369304 + -2.045461 2000-01-06 - 0.224374 - 0.738183 + -0.225712 + 0.301222 2000-01-07 - -0.026705 - 0.387631 + 0.042583 + -0.566633 2000-01-08 - -0.649027 - -1.247095 + -0.604785 + -0.859050 @@ -5871,43 +5871,43 @@

    5.4.1.3.2. Basics 2000-01-01 - 0.801076 - 1.465342 + 0.266217 + 1.228785 2000-01-02 - 0.348102 - -1.058147 + 1.488999 + 0.037730 2000-01-03 - 1.736169 - -0.839949 + 0.038978 + -2.140597 2000-01-04 - -0.435348 - 0.694244 + -0.079610 + -0.033805 2000-01-05 - -0.745863 - -0.692943 + -2.045461 + -0.369304 2000-01-06 - 0.738183 - 0.224374 + 0.301222 + -0.225712 2000-01-07 - 0.387631 - -0.026705 + -0.566633 + 0.042583 2000-01-08 - -1.247095 - -0.649027 + -0.859050 + -0.604785 @@ -5944,14 +5944,14 @@

    5.4.1.3.3. Attribute access -
    2000-01-01    0.801076
    -2000-01-02    0.348102
    -2000-01-03    1.736169
    -2000-01-04   -0.435348
    -2000-01-05   -0.745863
    -2000-01-06    0.738183
    -2000-01-07    0.387631
    -2000-01-08   -1.247095
    +
    2000-01-01    0.266217
    +2000-01-02    1.488999
    +2000-01-03    0.038978
    +2000-01-04   -0.079610
    +2000-01-05   -2.045461
    +2000-01-06    0.301222
    +2000-01-07   -0.566633
    +2000-01-08   -0.859050
     Freq: D, Name: A, dtype: float64
     
    @@ -6009,58 +6009,58 @@

    5.4.1.3.3. Attribute access5.4.1.3.3. Attribute access5.4.1.3.3. Attribute access -
    /tmp/ipykernel_3082/269534380.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
    +
    /tmp/ipykernel_3005/269534380.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
       df.two = [4, 5, 6]
     
    @@ -6290,14 +6290,14 @@

    5.4.1.3.4. Slicing ranges -
    2000-01-01    0.801076
    -2000-01-02    0.348102
    -2000-01-03    1.736169
    -2000-01-04   -0.435348
    -2000-01-05   -0.745863
    -2000-01-06    0.738183
    -2000-01-07    0.387631
    -2000-01-08   -1.247095
    +
    2000-01-01    0.266217
    +2000-01-02    1.488999
    +2000-01-03    0.038978
    +2000-01-04   -0.079610
    +2000-01-05   -2.045461
    +2000-01-06    0.301222
    +2000-01-07   -0.566633
    +2000-01-08   -0.859050
     Freq: D, Name: A, dtype: float64
     
    @@ -6310,11 +6310,11 @@

    5.4.1.3.4. Slicing ranges -
    2000-01-01    0.801076
    -2000-01-02    0.348102
    -2000-01-03    1.736169
    -2000-01-04   -0.435348
    -2000-01-05   -0.745863
    +
    2000-01-01    0.266217
    +2000-01-02    1.488999
    +2000-01-03    0.038978
    +2000-01-04   -0.079610
    +2000-01-05   -2.045461
     Freq: D, Name: A, dtype: float64
     
    @@ -6327,10 +6327,10 @@

    5.4.1.3.4. Slicing ranges -
    2000-01-01    0.801076
    -2000-01-03    1.736169
    -2000-01-05   -0.745863
    -2000-01-07    0.387631
    +
    2000-01-01    0.266217
    +2000-01-03    0.038978
    +2000-01-05   -2.045461
    +2000-01-07   -0.566633
     Freq: 2D, Name: A, dtype: float64
     
    @@ -6343,14 +6343,14 @@

    5.4.1.3.4. Slicing ranges -
    2000-01-08   -1.247095
    -2000-01-07    0.387631
    -2000-01-06    0.738183
    -2000-01-05   -0.745863
    -2000-01-04   -0.435348
    -2000-01-03    1.736169
    -2000-01-02    0.348102
    -2000-01-01    0.801076
    +
    2000-01-08   -0.859050
    +2000-01-07   -0.566633
    +2000-01-06    0.301222
    +2000-01-05   -2.045461
    +2000-01-04   -0.079610
    +2000-01-03    0.038978
    +2000-01-02    1.488999
    +2000-01-01    0.266217
     Freq: -1D, Name: A, dtype: float64
     
    @@ -6371,9 +6371,9 @@

    5.4.1.3.4. Slicing ranges17.1.2. Bootstrapping -
    -
    Mean Absolute Error : 19278.74181292808
    +
    Mean Absolute Error : 19574.44138484589
     
    diff --git a/ml-advanced/kernel-method.html b/ml-advanced/kernel-method.html index 3e5484c2bd..8c78ed4578 100644 --- a/ml-advanced/kernel-method.html +++ b/ml-advanced/kernel-method.html @@ -1857,7 +1857,7 @@

    20.1. Motivating Support Vector Machines

    -
    <matplotlib.collections.PathCollection at 0x7f2c3a3dd190>
    +
    <matplotlib.collections.PathCollection at 0x7fa8226de160>
     
    ../_images/kernel-method_9_1.png @@ -2044,7 +2044,7 @@

    20.3. Fitting a support vector machine
    <function __main__.plot_svm(N=10, ax=None)>
     
    @@ -2102,111 +2102,111 @@

    20.4. Beyond linear boundaries: Kernel S

    <function __main__.plot_3D(elev=30, azim=30, X=array([[ 0.11837111, -0.03203905],
    -       [ 0.12970811, -1.11256221],
    -       [-0.50409485,  0.7991212 ],
    -       [-0.72989295, -0.60055976],
    -       [-0.70025349,  0.90247824],
    -       [ 0.24392057,  0.05184432],
    -       [-1.17002276, -0.02014995],
    -       [ 0.47982751, -0.74048836],
    -       [ 0.98270926, -0.40842819],
    -       [ 0.81307859,  0.45405909],
    -       [ 0.83885964, -0.42451973],
    -       [ 0.04279497,  0.14551352],
    -       [ 0.06437443,  0.06546047],
    -       [ 0.15623371, -0.31632589],
    -       [ 0.18313996, -0.08364448],
    -       [-0.0030183 ,  0.04686915],
    -       [-0.88028578,  0.532819  ],
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    -       [ 0.02523009,  0.23085278],
    -       [ 0.21245994,  0.00394056],
    -       [-0.2694445 , -0.85134893],
    -       [ 0.52325357, -0.70273021],
    -       [ 0.03335592,  0.0422233 ],
    -       [ 0.00865885,  0.10951954],
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    -       [ 0.28605566,  1.20763581],
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    -       [-0.07959425, -0.09365631],
    -       [-0.11629632,  0.17092844],
    -       [-0.12805125, -0.00286549],
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    -       [-0.0016215 ,  1.15203074],
    -       [-0.05923185,  0.10452554],
    -       [-0.00539065, -0.00646125],
    -       [-0.09334799,  0.0862223 ],
    -       [ 0.0230368 , -0.37978209],
    -       [ 0.79888363, -0.64446565],
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    -       [ 0.07324116, -0.02666396],
    -       [-0.00371103, -0.12373911],
    -       [ 0.13356913, -0.07065824],
    -       [-0.09047764,  0.07384053],
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    -       [ 0.04829626,  0.14596582],
    -       [-0.08559119, -0.97526445],
    -       [-0.19804126,  1.1196651 ],
    -       [ 1.11037586,  0.36611865],
    -       [ 0.19373186, -0.20912239],
    -       [-0.45097522, -1.01775786],
    -       [-0.32505229,  1.1755452 ],
    -       [ 0.00938661,  0.11285819],
    -       [-0.11608994,  0.09930851],
    -       [ 1.01964571, -0.2813047 ],
    -       [-0.25504546,  0.04543063],
    -       [ 0.10152314,  0.08272095],
    -       [-0.87898966, -0.48385257],
    -       [-0.79378229, -0.47716821],
    -       [ 0.38252217,  0.68679926],
    -       [-0.05639158, -0.05225847],
    -       [-0.19008824, -0.86332183],
    -       [-0.07145021,  0.05680205],
    -       [-0.95197068,  0.00879897],
    -       [ 0.04477681,  0.08715884],
    -       [ 0.85692156, -0.2580265 ],
    -       [-0.18163865,  0.18257323],
    -       [ 0.56121819, -0.89225434],
    -       [-0.14069707,  0.06850504],
    -       [ 0.08698948, -0.00401784],
    -       [-1.01465911,  0.3396538 ],
    -       [-0.88714939,  0.63621048],
    -       [-0.155571  , -0.03980251],
    -       [ 0.13891479, -0.19425922],
    -       [ 1.09765538, -0.02026493]]), y=array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,
    -       0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
    -       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
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    -       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0]))>
    +{"version_major": 2, "version_minor": 0, "model_id": "b58222c37c0848848417a6aec4ffa1b6"}
    +
    <function __main__.plot_3D(elev=30, azim=30, X=array([[-0.14848999,  0.17733417],
    +       [ 0.170089  , -0.16879484],
    +       [-0.12331707, -0.92372479],
    +       [ 0.14978865, -0.03596801],
    +       [ 0.54440817,  0.66649003],
    +       [ 0.91876559, -0.79579236],
    +       [ 0.99706526, -0.1212961 ],
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    +       [-0.40885543,  0.92457735],
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    +       [ 0.01869547,  0.00222574],
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    +       [-0.53846659, -0.6694117 ],
    +       [-0.19470724, -0.0193433 ],
    +       [ 0.1165102 ,  0.2054175 ],
    +       [-0.00995049, -0.07288576],
    +       [ 0.81917904,  0.39846134],
    +       [-0.10683682, -0.07620444],
    +       [-0.18794649, -1.05788462],
    +       [ 0.01217664,  0.16336323],
    +       [ 0.13078778,  0.17426385],
    +       [-0.09784592,  0.17249158],
    +       [ 0.77522849, -0.17453352],
    +       [ 0.04382313,  1.01406812],
    +       [-0.14758293,  0.01950532],
    +       [ 0.02052251,  0.15664383],
    +       [ 0.84580961,  0.30452167],
    +       [ 0.02375428,  0.05969836],
    +       [-0.87961142, -0.52095595],
    +       [-0.03625464, -0.37240364],
    +       [ 0.21597874,  0.11993992],
    +       [-0.10742728, -0.11155046],
    +       [-0.97920397, -0.07369176],
    +       [ 0.01243166, -0.00583928],
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    +       [ 0.69960372, -0.83404985],
    +       [ 0.14727722, -0.02386957],
    +       [ 0.0060676 , -0.00954399],
    +       [ 0.21559742,  0.05595726],
    +       [-0.35348823,  0.90852496],
    +       [ 0.41841879,  0.82843148],
    +       [-0.16938064,  0.93768222],
    +       [-0.04690438,  0.01380554],
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    +       [-0.97751166,  0.16962453],
    +       [-0.57525185,  0.60186491],
    +       [-0.14739084, -0.04949411],
    +       [ 0.09804627,  0.18031459],
    +       [ 0.03500074, -0.02634081],
    +       [ 0.50786978, -1.04925824],
    +       [-0.08731882, -0.03156234],
    +       [ 0.18300793, -0.07047682],
    +       [-0.10543128, -0.17762792],
    +       [ 1.02388585,  0.26583684],
    +       [-0.0898721 , -0.95238612],
    +       [ 0.96755745,  0.50877145],
    +       [-0.07744817,  0.16905845],
    +       [ 0.84004275, -0.51798938],
    +       [ 0.41532714, -0.83113785],
    +       [ 0.14532358,  1.07332987],
    +       [-0.89788068, -0.26664917],
    +       [-0.03302014,  0.82516437],
    +       [-0.55799765, -0.84728473],
    +       [ 0.19168024,  0.02961706],
    +       [-0.5789342 , -0.80691691],
    +       [-0.05418754, -0.04537884],
    +       [ 0.07885114,  0.0731486 ],
    +       [-0.13009751,  0.01559291],
    +       [ 0.09425421, -0.06187374],
    +       [-0.14579523,  0.00605812],
    +       [ 0.02699234, -0.08634388],
    +       [-0.93930447, -0.62691627],
    +       [-0.08485438, -0.13741474],
    +       [-0.77118415, -0.61881126],
    +       [ 1.03394629, -0.09440484],
    +       [-0.91165711,  0.20099994],
    +       [ 0.19663177,  0.25672968],
    +       [ 0.95074758,  0.61759966],
    +       [ 0.16717129, -0.04242818],
    +       [-0.22838073,  0.96231377],
    +       [-0.26222688, -1.01146794],
    +       [ 0.72553862, -0.39029111],
    +       [-0.06584067,  0.05428221],
    +       [ 0.225225  ,  0.04883436],
    +       [-0.10351476,  0.067387  ],
    +       [-0.51261137, -0.98922493],
    +       [-1.01819662,  0.1151869 ],
    +       [ 0.26803518, -1.04187376],
    +       [-0.49242864,  0.882162  ],
    +       [ 0.80821987,  0.79829679],
    +       [ 0.07523394, -0.13732133],
    +       [ 0.03547556, -0.03405817],
    +       [-0.00290804, -0.18180785]]), y=array([1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0,
    +       1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,
    +       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,
    +       0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
    +       0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1]))>
     
    @@ -2239,7 +2239,7 @@

    20.4. Beyond linear boundaries: Kernel S

    -
    <matplotlib.collections.PathCollection at 0x7f2c37ed18e0>
    +
    <matplotlib.collections.PathCollection at 0x7fa8202478b0>
     
    ../_images/kernel-method_39_1.png @@ -2366,7 +2366,7 @@

    20.15. 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yWcBqBfJ5VANYfPNbMfe//xl6bZ3ZVRJRCchmFxGLxaAoEQwPD0HXdQCFu33cbjc0TTN2pq2urjGxUioHDCy0evk8vO89BMdPD0PQl3aMXL7mDMBx+Eeo6VEx86OfQa/ymlcnEZWEn/70JxgY6DO+bmlphSSF4fcH4XQ6zSuMyhIDC62a84ffh/Mnj570cSGfh1VR4P78P2PhDvaQItpKpqePQZYj2L17DzyewvqTYDCE2dk4RDEMUZTg9VabXCWVMwYWWjXXl/4PdIsVgpY/6fcIWh7ur34ZCx/9GGC3b2B1RLTRFhaSUFUFqipjcnISAOB2u7F373kAgFBIhChKXJdC64KBhVZH12F/7jenDCvLLNPHYO3vQ36ppwcRbR65XA59fTHIsoyhoQFoWuHysNVqRWenD42NTcb3Lq9PIVoPDCy0OroOaGfQ6XRpbQsRlb/lxbJAIbA88cRjxp4pzc3bIIphBAJBuN1us0qkLYCBhVbHYkE+EIS1JwrhuDevE9FdLuSXdqUkovI1MxOHosiIx+N405uuBgC4XC7s2LELdrsdoigZfX6Iio2BhVYt9d4PoPJv/+aU36NbrUjfdAvATZ+IylIqlUJPTxSKEsH4+JgxHo/HUVtbCCcXX3ypWeXRFsbAQquWvukWuL/ypcJZlvxr17LoViv0qios/F8f2fjiiGjNdF3H+PgYXnzxdxgY6Ed+aX5bLBa0t3dCkrjzLJmPgYVWr7ISM9/9IaoP3QT7C7+DbrMBuRyEpc3jtJZWzD70bWidPrMrJaLT0HUd+XweNlvhz0AymURvbwwA0NDQCFGUEAqJqKjwmFkmkYGBhc6I3tyMmcNPwP7sM3B+51uwTk7A0VCH+auuQerAnwA2/koRlbJEIrG0RX4EwaCICy7YB0EQ4PN14bzzzkcwGEJ9fYPZZRK9Bv+60JkTBGT3X4zs/oths1ngqPUgG08CuTO4i4iINkwmk0EsFoUsRzA6OmKM9/bGcMEF+wAUtsvft2+/WSUSnRYDCxHRJvbkk49DUWTjNmRBENDW1g5RDMPv95tcHdHqMbAQEW0Suq4jHp9GbW2dsbuspunI5XKora2DJIURCnEBLZUnBhYiojI3Pz8PVVWgKBFMTx/DddfdiKamZgDA3r3nYefOXWhsbOIW+VTWGFiIiMpQNruIWCwGVZUxNDRo7EZrs9kwPX3MCCzLe6cQlTsGFiKiMjM9fQyPPPJtZLNZY6ylpRWiKCEQCMHpdJpYHVFxMLAQEZW46eljmJubg8/XBQCoqamFw+FARUUFRDEMUZTg9VabWyRRkTGwEBGVoFRqwViXMjk5CY/Hg3e+8y9gsVhgsVjwtrfdgMrKSq5LoS2DgYWIqERks1n09/dBUSIYHByAttQh3WKxoKmpGZlMGm53BQCgqqrKzFKJNhwDCxFRifjtb5/FkSMvGl83NTVDFMMIBkNwu93mFUZUAhhYiIhMMDMTh6rK6OzsQnPzNgBAICCip6fH2C+Fd/gQvYqBhYhog6TTaUSjKhQlgvHxMQDA/HzSCCxNTU145zvfxXUpRCfAwEJEVES6rqOvrxeKEkF/fx/y+TyAwhb5HR0+486f5TEiOjEGFiKiInvmmf9EIjELAGhoaIQoSgiFRFRUeEyujKh8MLAQnUwyCedPfwzL5AQ0bzUWD74JekOD2VVRCUskElBVGYOD/Xj3u28FUDhrsnv3HszNzUGSwqiv5+8Q0VowsBD9sXweFf/4P+H+P1+AJZmEbrFA0DToNhvS19+E5P/8HPRK3lJKBZlMBrFYFIoiY2RkGABgsQjo7e1FQ0MrAOCcc/aYWSLRpsDAQnQ8XUfVbR+E85FvQ1jqzSIs7YUh5HJwffubsL3yMma+/2PAw9P5W9n09DSef/436OvrRS6XA1A4m9LW1o7t28Po7OzEwkLO5CqJNg8GFqLjOA4/Ctd3Hz7p40I+D9sffo+K+/4FCx/92AZWRmbTdR25XA52u90Yi0ZVAEBtbR0kKYxgUERVVRVsNgucTicDC9E6YmAhOo77S/dBt1ohLN3JcSKCpsH9wBex8NcfBWycQptdMjkPRSlskV9XV4crr7wKAFBXV4eLLno92tra0djYxDt8iIqM77ZEy3Qd9l89fcqwsswyOQFrXy/ywdAGFEYbLZtdRG9v4VbkoaFB6EuXB5PJeeRyOdiWguq5577OzDKJykI2m0VfXwyKIuPmm6+D1epa0/NseGBJJpO4+uqrMT4+ju985zs455xzNroEopNbRVgxLC4Wrw4yzW9/+2scOfICstmsMdbS0gpRlOD3B42wQkQnp+s6RkaGoSgyYrEoFpfeL7PZLFyuMgksX/jCF4yNk4hKiiAg39UNa1+vseD2ZHS7A1pHxwYVRsU0PT2NqqpK2O0OAIDNZkc2m0V1dTVEMQxRlOD1VptcJVF5iMfjUJQIFEXG/PycMe71VkMURbhca++JtaGBpaenB//2b/+Gj33sY/jUpz61kS9NtCrpd78Pnk/991N+j261In3djdCrvBtUFa23VGoB0agKWY5gcnICBw5cCUkKAwDC4TC2bduGbdtauC6FaBVSqZTRcmJiYtwYdzqdCASCCIUktLS0QhAEWCyWNb/OhgaWz3zmM7j55pvR3d29kS9LtGrpWw7B/eX7YRkaPOFaFt1ihe5yIfXXt5tQHZ2NXC5nbJE/ODgAbel2dYvFYuxCCwBudwXc7gqzyiQqC7lcDv39fVCUCAYG+lfMp46OTohiGF1d3et6CXXDAsvhw4ehKAruuecevPzyy+vynFbr2pMarY/lY7BpjkVtDeb+4zCqbnwbrJGjxh1DxuZxdbWY/8Z3IEhSya1Y33THYh1lMhk89NDXkE6njbGWlm3Grchu99pPU58Ij0Xp4LFYP7quY2xsDLIcQU+PumI+bdvWDEnajmAwhIqKkwf+szlpuSHvualUCnfffTduv/12VFZWrtvzer3r+yZDa7epjkWtBPzhJeAnP4Hw4IPA8DCE2lrg+uthueEGeNe4YGyjbKpjsUbxeBwjIyPYuXPn0ogHHR2tmJ2dxfbt27Fz507U1dUVvQ4ei9LBY7F2MzMzeOWVV3D06FHMzMwAKASPxsY6Yz7V19cXvY4NCSz33nsv6uvrcd11163r8yYSKeTz2ro+J50Zq9UCr9e9OY/F/jcU/ne8VB5IJc2p5zQ29bFYhXQ6baxLGRsbhcViQU1No9Fg8JJLDsDtdhvrUuLx4h3HrX4sSgmPxdpkMhljPo2OjhjjdrsdgUAQoiihra3dWJOy2vlUXe1e8zqWogeW4eFhPPDAA/j85z+PubnCiuGFhQXj/5PJJDxr3OI8n9eQy/EXsBTwWJSOrXQs8vk8Bgb6oSgR9Pf3GXcgCoKA1tZ2JJNpOByFT9YOhwv5vA7g1HeArW99W+dYlDoei9PL5/MYHOyHoijo71/ZcqK9vQOhkAS/32/cUadpMNaurNZpbsA8paIHlqGhIWSzWXzgAx94zWOHDh3Cnj178PDDJ98KnYjoZGT5KJ588gnj6/r6BoiihFBIhMezfpefiTYrXdcxOTkBRZERjapIpRaMx+rq6iGKYYRC4rou51irogeW7du34+tf//qKsaNHj+Kzn/0s7rrrLm4cR0SrkkgkoKoyqqtrEFzaYdjvD+J3v3sefn8AoiihoaHR5CqJysPc3BxUVYaiyIjHp43xiooKhEIiRDGM+vqGkrq1v+iBxev1Yt++fSd8bOfOncctiiMiWmlxcRGxWBSyHMHIyDAAoLl5mxFYXC4XbrnlUEm9qRKVqsJ86oGiFObTcssJm82Grq5uiGIYHR2dZ7VXSjGV2p2ZREQYHByALB9Fb29sxXX01tY2iGIYuq4bIYVhhejkNE3D0NAgVFVGLBZDLvdqy4nW1jZIUhjd3QE4nU4Tq1wdUwLLvn37IMuyGS9NRGXgpZeOoL+/DwBQW1u7dB1dQlVVlbmFEZWJY8emIMsRRKMKkslX7+Cpqakx5pPXW167dfMMCxGZJpmch6oqUFUFb3rTm4030J07dy31HpHQ2NjEsyhEq7CwkISqKlAUGVNTk8a4y+VCIBCCJG1HU1P5zicGFiLaUNnsInp7C1vkDw0NGtfRVVXG6153AQDA5+uGz8cWHkSnk81m0dcXg6LIGBwcMOaT1WqFz9cFUQyjs9MHq9VqcqVnj4GFiDZEMjmPX//6WcRiUWSzr15Hb2lphShK8PuDJlZHVD50XcfIyDAURUYsFsXi4qLx2LZtLRBFCYFACK4S35X7TDGwEFHRLC4uwuEobDLlcDiMsLJ8uUcUJVRX15hbJFGZiMfjUJQIFEXG/PycMV5V5YUkSQiFJNTU1JpYYXExsBDRukqlFowtvfP5PG688e0QBAF2uwOXXPIGVFdXY9u2lrK9jk60kVKpFKJRFYoSwcTEuDHudDoRCAQRCkloaWndEvOJgYWIztqpWs3PzSXg9VYDAMLh7WaWSVQWlueTqsro7+9bMZ86OjohimH4fF2w2+0mV7qxGFiI6KwcPfoyfvWrp5HJZIyxxsYmSFIYwWAIbvfJW80TUYGu6xgfH4OiRNDTE0U6nTYea2xsNLbI38rziYGFiM7I7OwMbDab0aunosKDTCaDyspKY3+Huro6k6skKg+JxCwURYaiRDA7O2uMV1ZWIhQqrPOqq6s3scLSwcBCRKeVTqfR06NCUWSMjY3i3HNfh4suej0AoKOjE3/6p29Da2vblriOTnS2MpmMMZ9GR0eMcbvdvtQXK4zW1raS3SLfLAwsRHRC+XweAwP9UBQZ/f29yOfzAApb4S8svNrR1WKxoK2t3awyicpCPp/H0NAAZLkwn45vOdHe3gFRDKO7uxt2u8PkSksXAwsRvYau6/j2t7+BeDxujNXXN0AUJYRConE5iIhOTtd1TE5OGLs5p1KvBv26unpjXUplJefTajCwEBHm5ubQ1xfDrl27IQjCUqPBdmQyGeM6ekNDo9llEpWF+fl5qKoMWY4gHp82xt3uCoRCojGfeAn1zDCwEG1RJ2o139DQiJaWVgDAvn37cckll/E6OtEqZLOLiMViUJQIhoeHjC3ybTYburq6IYphdHR0cj6dBQYWoi1kudW8okTQ29u7otX8H69DKYd280Rm0jQNw8NDUJQIYrHYivnU0tIKSQrD7w9yLq0TBhaiLWRsbBQ/+tEPjK9ra2sRCkll2WqeyCzT08cgyxGoqoxkMmmM19TULF1CDXM+FQEDC9EmlUzOQ1UVAMDevecBKHzqa2xsQnPzNohiuKxbzRNtpIWFJFRVgaLImJqaNMZdLhcCgRAkKYympmbOpyJiYCHaRLLZLBRFxtGjR41W8y6XG+ecswdWqxWCIOC6627kmyrRKmSzWfT390KWZQwNDRhb5FutVvh8XQiFJHR2+mCz8U/pRuB/ZaJNYGxsFLL8CkZGBpBIJKFphQV/y63mlxcAAmBYIToFXdcxMjIMVVXQ06NicXHReGz5zGQgEITb7Taxyq2JgYWoTOm6boSP3t4YIpGjcLsd8HqrEQwWbp2srq4xt0iiMhGPx6GqMhRFxtxcwhivqvJCFAu39tfU1JpYITGwEJWRVGoB0agKWY7gwgsvQmenDwAgSWHk81lceOF5cLurkc/rp3kmIkqlUohGVaiqjPHxMWPc4XAgEAghFBLZcqKEMLAQlbjlVvOKEsHAQL9xHV1RIkZgqaurxxvfeAC1tR7E40kADCxEJ5LL5ZZaThTm03LLCYvFgvb2TkiSBJ+vG3a73eRK6Y8xsBCVqFwuh6ef/iV6eqLIZDLGeGNjEyQpjGAwZGJ1ROVD13WMjo7i6NGj6OlRkU6njccaGxshioX5VFHhMbFKOh0GFqISkk6n4XK5ABTuRBgbG0Mmkzmu1XwYdXV1JldJVB4SiVn09KgYHIxhdHTCWIzu8XgQCkmQpDDq6upNrpJWi4GFyGTpdBqxWBSKIuPYsSkcOvQe2O12CIKAiy56PaxWK1vNE61SJpNBLBaFLEcwOjoCi0WA2+2AzWZHd7cfohjmfCpTDCxEJsjn8xgc7DdazS9fRxcEAePjY2hv7wAA+HxdJlZJVB7y+TyGhgaM+ZTL5QAU5lN7ewcuuOBcNDa2QhD4J6+c8egRbbCBgX489tjPkE6njLG6unpIUqHVvMfDVvNEp6PrOqamJqEoMlRVQSq1YDxWW1u3NJ8k1NR4jcXouZxmYsV0thhYiIpsbm4O2WzWWHtSU1ODdDqFiorlVvNh1Nc38NZJolWYn59f2iI/gunpY8a42708nyQ0NDRyPm1CDCxERbC4uIhYrAeKEsHIyDB8vi5cffU1AACvtxpve9v1aGpq5nV0olXIZhcRi8WgKBEMDw8ZOzfbbDZ0dXVDFCW0t3fCarWaXCkVEwML0TrRNA1DQ4NQlAh6e3tXtJrP5/PQNM0IKNu2tZhVJlFZ0DQNw8NDUFUZPT09K+ZTS0srJCkMvz8Ip9NpYpW0kRhYiNbJ4cM/Qn9/n/F1bW0tQiEJoZDEVvNEqzQ9fQyyHIGqykgmk8Z4TU3N0q39ErzeahMrJLMwsBCtQTJZuI4eDu8w9k3p6PBhfHwcwWAIohhGU1MTr6MTrcLCQhKqqkBVZUxOThrjLpcLgUAIkhRGU1Mz59MWx8BCtErZbBZ9fTEoiozBwQHoug6Hw4EdO3YBAMLh7dixYyevoxOtQjabRX9/L2RZxtDQgNFywmq1orPTB1EMo7PTB5uNf6aogL8JtHXk8xASs9CdLqCiYlU/stxqXlFkxGLRFa3mt21rQcVxz8PeI0SnVtgifwSKIqOnR10xn5qbt0EUwwgEgnC73SZWSaWKgYU2PcvIMNz33wvXg1+FZaltfPa885F6/4eQedv1wCnu1EmlFvAf//Hvxl0JXm+10Wq+urpmI8onKnszM3EoigxFkTG3NAcBoKrKa8ynmppaEyukcsDAQpua7aUjqP7zt0KYn4OwtJssANhe/B28f/k+pH96GHNf+CJgtRqt5mdnZ3DJJZcBACoqPAgGQ7Db7RDFMLZta+F1dKJVSKVS6OmJQlEiGB8fM8YdDgcCgRBEUUJLSyvnE60aAwttXskkqm9622vCCgAIy9fL//27GGluxpH9F2NgoB+apkEQBOzZcy6qqqoAAAcPvmnDSycqR/l8HgMD/VCUCPr7+4yWExaLBe3tnZAkCT5fNy+f0powsNCm5Xrk2xCmpnCiz2+TFgtetNuh2O1IffdhLDQ1A1ar0Wre4XBseL1E5UjXdUxMjEOWI+jpUZFOp43HludTMBhCRYXHxCppM2BgoU3L+Z1vAYIALK0/0QEjvIxbLPj9UiipWljATqsV/pvewVbzRKuUSMwu9fGRMTMzY4x7PB5jv5T6+gbzCqRNh4GFNi3L1CTSug7VbscrNhuCuRzOzxZ2ywzlchjOZhHOZtGRz2O+rh4ZhhWiU8pkMojFopDlCEZHR4xxm80Ov98PUQyjra2dLSeoKIoeWJ588kl88YtfRDQaxfz8PJqbm3Hw4EHcdtttxhoBovWUz+cxONiPp1xuDFRVYXn1SlYQjMDiBPCm405d60uNCYloJU3TMDg4AEWJoK+vF7lcDgAgCALa2tohimH4/X7Y7byMSsVV9MAyMzOD3bt349Zbb0VNTQ1UVcU999wDVVXxwAMPFPvlaYv51a+eRiRyFOl0CrZgCI6+GBryeezM5RDOZk/4M5q3GouXvnFjCyUqYbquY2pqcumSj4JUasF4rLa2DpIURjAo8kMnbaiiB5Zrr712xdf79u2Dw+HAJz7xCYyPj6O5ubnYJdAmtrCQXLGYL5FIIJ1OoaKiAqEbb8a+XzyOpuQCLFr+hD+vCwJS7/8QsLS9PtFWNj9faDmhKBFMTx8zxt3uCoRCIkIhEY2NbDlB5jBlDUtNTQ2AwtbMRGdqcXERsVgPVFXG8PAQbrrpFtTWFjadOvfc8xAOb0dHRycsFgts3/gOcMO10BcWVtzarFssgKZh8eprsPDRj5n1r0Jkumx2EbFYDKoqY2ho0Ngk0WazwefrhiRJaG/vZMsJMt2GBZZ8Po9cLodoNIrPf/7zOHDgANrb2zfq5anMLbeaV5QIYrHYilbzo6PDRmBpalp5xi533vmIP/403P/n83D/24MQFgqntvPhHUh94C+RvukdAN+IaYvRNG2p5UQEsVjPig+PLS2tEEUJgUAITqfTxCqJVhL05ThdZJdddhnGx8cBAJdeein++Z//eUUflrVIJFLI57X1KI/WyGq1wOt1F/VYTE1N4Yc//P5rWs1L0valVvPe1T3R4iKEY8cAlxN6TW3hludNZCOOBa1OqR6LY8eOQVEiUBQZ8/Pzxnh1dQ1EUYIkhVFdXW1iheuvVI/FVlVd7V7zXWQbFlgikcjS1udR3HvvvWhvb8dXvvIVnmak10gmk5idnUVraysAIJfL4b777oMgCJAkCTt27EBLC7fIJ1qNhYUFHD16FK+88gomJiaMcafTacyn1lZukU+lb8MCy/EikQiuvfZa/NM//ROuuuqqNT8PE7P51uvTSzabRV9fL2Q5gsHBAVRVeXHLLbcab6JTU5Oora1jwD0FfpIsHWYfi+X5pCiy0XICKGyR7/N1QZLC8Pm6YLNt/q24zD4WtNLZnGEx5bdVkiTY7XYMDAyc1fPk8xpyOf4CloK1HAtd15euo8uIxaIrWs07HE7Mzy8YbeZrauqh6+DxXgXOi9KxkcdC13WMjo5AUWT09Kgr5lNTU7OxRf7ynAK21nzivCgNZ3OKxJTAcuTIEWSzWS663eKeeeY/8fvfv2h8XVXlhSRJCIXYap5otWZm4lBVGbIsY24uYYxXVlZBksIIhSRjUTpROSt6YLntttuwa9cuSJIEl8uFSCSCL3/5y5AkCQcPHiz2y1OJKKxfUtHW1mb06+nq6kYk8gpbzROdoXQ6jWhUhaJEMD4+Zow7HA74/UGIooTW1jbOJ9pUih5Ydu/ejUcffRT3338/dF1HW1sbbrjhBrz3ve9lR9xNLpfLrWg1r2kadu/ei4svvhQA0NrahkOH3sNW80SrkM/nV8yn/NK+QoIgoKOjE6IooavLz/lEm1bRA8sHPvABfOADHyj2y1CJ0HUd4+NjUJQIenqir2k1X1//aoNBQRD45kp0CrquY2JiHIoiIxpVkU6njMcaGhohihKCwRA8nkoTqyTaGJt/iThtKF3X8ZOfPIqFpQ3allvNS1LYuBRERKeWSCSgqjIUJYKZmRlj3OPxIBgUIUlh1Nc3mFcgkQkYWGjNMpkMZLkHx46N4ZJLDgAo3Da5Y8dOzM3NQRTDaG1tY6t5olXIZDKIxaJQFBkjI8PGuM1mh9/vhyiG0dbWzvlEWxYDC52RfD6PoaEByLKM/v5eaFoebrcD3d0iWloKd31dcMFFJldJVB40TcPg4AAUJYK+vl7kcjkAhculra1tEMUw/P4A1/sRgYGFVimRmMVLL/3+Na3mGxsbcP75565Ym0JEJ6frOqamJqGqMlRVMS6fAkBtbR0kKYxgUERVVZWJVRKVHgYWOild143bIlOptLFnynKreVGUsG1bM+rqKhGPJ7kpE9EpzM/PQ1UVKEoE09PHjHGXy23Mp8bGJt6KTHQSDCy0wnKreUWJwOv14g1vKKxNaWpqwu7de9He3oH29g5ji3y+uRKdXDa7CFXtgaJEMDQ0iOVOKDabDT5fNyRJQnt7J1tOEK0CAwtB0zQMDw8ttZqPIZcrtJp3Op245JI3wGq1QhAEY/8UIjq5wrqUITz7bB9+//uXkcm8ukV+S0srRFFCIBCC0+k0sUqi8sPAssUdOfICjhx5Aclk0hirqamBKIYRCon85Ee0StPTx6AoMlRVxsJCEm63A9lsFtXV1RDFMERRgtdbbXaZRGWLgWWLWVhIwul0GUFkcTGDZDIJl8uFQCAESQqjqamZl3qIViGVWjDWpUxOThrjbrcLu3efg7a2bjQ0cF0K0XpgYNkCstks+vt7IcsyhoYGcOWVV8HvDwAAwuGdqK9vRGenb0u0mic6W7lcDn19vVCUCAYHB6BphcXmFosFPl8XQiEJgYAfjY3VXIxOtI74F2qT0nUdIyPDUFXlNa3mJybGjcBSVVXF2yeJTkPXdYyOjkBVZfT0RJHJZIzHmpqaIYphBIMhuN1uAIDNxs3diNYbA8smlE6n8Z3vfGtFq/mqKi8kSUIoJKGmhq3miVZjdnYGihKBoihIJGaN8crKKoiiBFEMo7aW84loIzCwbAKpVAqTkxPo7PQBAFwuF1wuFzKZNAKBEERRQktLK6+jE61COp1GT48KRZExNjZqjNvtdmM+tba2cT4RrUImk0FPj4q+vl686U1vPqvnYmApU7lczmg1PzDQD0EQcOjQe4xbJQ8e/BN4PJXshky0Cvl83phP/f19yOfzAAr7DHV0dEIUJXR1+TmfiFYhn89jcLAfiqKgv//VlhODg/1obDxnzc/LwFJGdF3H+PgYFEVGT4+KdDptPNbY2Ihkct4ILLzsQ3Rquq5jYmICihJBNKoinU4Zj9XXN0AUJYRCIjyeShOrJCofiUQCv//9i4hG1RUtXOrq6iGKhTtQzwYDSxl5+eU/4KmnfmF87fF4EApJkKQw6urYy4doNRKJBKLRwq3I8XjcGK+oqEAoJEEUJTQ0NJpYIVH50DTN6CCey2Xx0ktHABRauIiiCFEMo76+YV0uoTKwlKjlVvOVlVXo6OgEAHR3d+PXv34G3d2FVvOtrW1sNU+0CouLi4jFopDlCEZGho1xm82O7u5uiGIY7e0dnE9Eq1CYT4WWEx6PB1dc8ScACmdS9u49D62tbejo6Fz3+cTAUkLy+TyGhgYgy7Jx3a+jo9MILB5PJd71rvdyvxSiVdA0DUNDg1CUCHp7Y8Z1dEEQ0NraBlGU4PcH4XA4TK6UqPSdrIWLzWbHZZdljfVd+/dfXLQa+JevBExOTixt6a2suO5XW1uH9vaOFV2TGVaITm1qahKKEoGqKlhYOH4+1S61nJC49xDRGfj971/Eiy/+7iQtXKQNW4zOv34l4Jln/tM4Te12Vxit5hsaGnnrJNEqJJPzS1vkyzh2bMoYd7ncCIVEhEISmpq4RT7RaiwsJGG3O4wgks/njRYuwWDh75MZLVwYWDZQNruIWCwGVZVxxRVXwu2uAABs374TFRUVEEW2midarWx2Eb29hS3yh4YGoes6AMBqtcLn64YkSejo8HE+Ea1CNptFX18MiiJjcHAAV1xxJUIhCQAgioUNRzs7zZ1PDCxFpmkaRkaGl6779SCbLVz3U1UFu3fvBYClHTMlE6skKg/LLSdkOYJYLGrMJwBoaWlFKCQiEAjB5XKZWCVReVieT4oiIxaLrmjhMjk5YQQWj6cS3d3m397PwFIkqdQCXnzxBaiqvOK633Kr+a4uv4nVEZWX6enppXUpMubn541xr7faCPzV1TXmFUhUZrLZRXzrW99Y0cKlMJ/Ekm3hwsCyjo6/Hx0Q8NJLR5DP5+FyuRAIhCBJYVOu+xGVo1RqAdGoClmOYHJywhh3Op0IBIIQxTC2bWvhfCJahVQqhYmJMfh83QAAu92ByspKLC5mymY+MbCcpWw2i/7+XsiyjGx2EX/2Z9cBANxuNy64YB+qq2vQ2enj3T1Eq5DL5dDXV1iXMjg4AE3TAAAWiwWdnT6IYhg+XxfnE9Eq5HI59Pf3GS1cAODQofcYXcUPHDgIt7uibFpOcNavwXKr+eUt8o+/7jc3N2fcMnnuua8zq0SisqHrOsbGRqEoEfT0RJHJZIzHmpqaIYoSgsGQsUidiE7u1RYuhfl0ohYuy4HF6602q8w1YWA5Q6oq49e/fnbFdb+qKu9S3xHu70C0WrOzM1CUCBRFQSIxa4xXVlYt3dofRl1dnYkVEpUfWT6KJ554zPi6srLSaDlR7i1cGFhOI5VKQRAE464DQRAwN5eAw+GA3x9kq3miM5BOp9HTo0JRZIyNjRrjdrsdfn8QkhTmfCJapUwmg54eFRUVFcaNHD5fN5xOJ7q6ujddCxcGlhPI5XJGq/mBgX6cf/6FOO+88wEUfhmuvPJN8Pm6y+a6H5GZ8vn80nwqtJzI5/MACuG/o6MToiihq8vP+US0Cidq4dLS0moEFrfbjXe9672bcv8hBpYlhVbz45DlCHp61BXX/SYnJ41/ttvtCAZFM0okKhuF+TQBRYkgGlWRTqeMx+rrG5YuoYrweMzf24GoHExOTkCWC/Pp+BYudXX18Pm6V7Rw2YxhBWBgAVB4c/3ud7+1Iph4PB7jul99fYOJ1RGVj7m5OaiqDEWJIB6PG+MVFRXGfGpoaDSxQqLy9JvfPGvc6eN2Vxj7pWylFi5bMrBkMhkMDg4gGAwBKJyarqurRzw+A7/fD1EMo62tfdNc9yMqpkKr+ShkOWL0xAIKXVy7uwvX0dvbOzifiFZhuYWLokTwxjdeYdzIEQ7vgMPhgCiG0dHRuSXn05YJLJqmYXBwAIoSQV9f4bpfTU2N8Wlv3779uPTSN8BuZ6t5otPRNA1DQ4NQlAh6e3uNVvMA0NbWDlGU4PcH4XBwPhGdjqZpGB4eWmrhEjPmUzSqGNtjBAJBBAJBM8s03aYOLLquL7Wal6GqyorrfrW1dSvWqfBaOtHpFeZTBKqqYGHh+PlUa7Sa5639RKuTSqXw4ou/e00Ll5qaGohiGIFAyMTqSs+mDiwjI8P4wQ++Z3ztdlcgGAxBFCU0NrLVPNFqJJPzUFUFiiLj2LEpY9zlciMUKlxHb2rifCJajXw+byyKtVqt+MMfXkIul2ULl1XYNIFl+bqfrusIh7cDALZta0FlZRWam7dBkiS0t3du2tXTROspm11Eb28vVLXQal7XdQCFN1ifrxuSJKGjw9xW80Tl4vgWLqnUAq6//iYAgMPhwL59F6GqyovOTs6n0ynrwHL8dfRYrAfZbBaVlZWQpDAEQYDVasU73nErfwmIVmG51bwsRxCLRZHNvrouZdu2FoiihEAgZGyiSEQntzyfFEVGLBZd0cJlZiZudEPevXuvSRWWn7INLKlUCg8++FUkEnPGWHV1NUQxjFwuZ2xCxbBCdGrT09NLtyLLmJ9/dT4VWs0XbkWurq4xr0CiMhONKnj22V+9poWLJBVauCyHFTozZRtYMpkM5ufn4XQ6l9alhNHcvI3X/YhWIZVaQDSqQpYjmJycMMadTmfZtJonKhWpVGFjxOWmgjabzWjhEggU1k22tLRyPp2lsg0sdrsdV131ZrS1dbLVPNEq/HGreU3TAAAWiwWdnT6EQhK6uro5n4hW4Y9buOzdex4uvPAiAEBHhw9XXnkVfL4utpxYR0V/Z/rxj3+MH/zgB3j55ZeRSCTg8/lw66234rrrrjurtOnxeBAIBJHLaetYLdHmcnyr+WhURSaTMR5rbGyCJIURDIbgdleYWCVReTh+PvX0RFdsjTE9fcz4Z6vVamxMSuun6IHlq1/9Ktra2nDHHXegtrYWzzzzDD7xiU9gbGwMt912W7FfnmhLmp2dWdp/SMbs7KwxXllZaeyXUldXZ2KFROVF13U88si3MTExboxVVlYaLSfq6upNrG5rKHpguffee1e8Me7fvx8zMzP4yle+gv/yX/7LltxemKgY0uk0fv/7Hvz2ty9gePjVLfLtdjv8/iAkqdBqntfRiU4vk8mgv78PoZAIQRAgCAIaGhoQj0/D7w9AFAvziX/DNk7RA8uJPsVt374dDz/8MBYWFlBZyR1midYqn89jcLAfsixjcLAPDocVqdQiBEFAR0cnRLGwLoUtJ4hOL5/PY2hoALIso7+/0MLF6/Vi27YWAMAFF+zD619/CeeTSUxZXff888+jubn5rMOK1cpka7blY8BjsXF0XTdazRdaThTuULBYBDQ0NKOrq9BzhO0mzMN5UTpOdyyWW7jIcgSKsrKFS0NDPTQtD5ut8LNeL9tOnK2zOcG74YHlueeew6OPPoqPfexjZ/1cXq97HSqi9cBjUXyJRAJHjx7FK6+8gunpaWO8oaEW4XAYO3bsQFNTk4kV0h/jvCgdJzsWw8PD+I//eMT4ur6+ZsV84iXU0rGhgWVsbAy333479u3bh0OHDp318yUSKeTzvEvITFarBV6vm8eiSBYXFxGLRSHLEQwPDxtb5NtstqXr6JLRan75EySPhfk4L0rH8ccinU6jp6cHuVwOu3adAwBwu6vhdFagoaERkhRGR8erLVxmZhZO9dS0BtXV7jWv+9mwwJJIJPD+978fNTU1uOeee9ZloVI+r/G25hLBY7F+jm850dvba7SaB4DW1jZIUhh+fxAOh2Pp+2HsqQLwWJQSHgvzaZqGvr4+/OY3v0M02oNcLgu3uwKhUNgIJjfc8A7jb5Kug8esiJY+c63JhgSWdDqND37wg5ibm8O3vvUttp8nOoFjx6YgyxFEo8oJW82HQhK8Xq+JFRKVj+npacjyUfT0KNC0LFKpRWiajpqaGoRCEjTt1a7JvNOnPBQ9sORyOXzkIx9BLBbDQw89hObm5mK/JFHZWFhIQlUVKIqMqalJY9zlchstJ3gdnejMRSIv48iRF2GxCKit9SIQkBAMSmhqauZ8KlNFDyx33XUXnnjiCdxxxx2Yn5/Hiy++aDy2Y8cO47Q20VaRzWbR1xeDosgYHBww1qVYrVb4fF0QxTBbzROtUjabRX9/L2RZxp49e9He3gEAEMUwEokEtm/fjr17d2JuLsNLPWWu6IHl6aefBgDcfffdr3nsscceQ3t7e7FLIDLdqVrNNzdvgySFEQiE4HK5TKySqDzouo7R0REoioyeHtWYT263ywgsDQ2NuOqqt8Bmsyz1x8qc4hmpHBQ9sDz++OPFfgmikhWPx6EoESiKjPn5OWPc662GKIpsNU90BnK5HH73u+egKDLm5hLGeFWVF6JY2CKfNi+2ZSVaZ6lUCtGoCkWJrOg74nQ6EQgEIYphbNvWwuvoRKuQy+WMDuJWqxWxWBRzcwk4HA4EAiGIooSWllbOpy2AgYVoHeRyOfT39xmt5pdvM7ZYLEtb5IfR1dVtvPES0cnlcjkMDPRDUSIYGxvDLbccgt1uhyAIOP/8fQB0+HzdsNvtZpdKG4jvnkRrdHyr+WhURSbz6jXyxsYmSFIYwWAIbneFiVUSlQdd1zExMQ5ZjqCnR0U6nTYeGxkZhs/XBQAIBkMmVUhmY2AhOkOzszNQFBmqKmN2dtYYf7XVfPiETT+J6MSGh4fwy18+gZmZGWPM4/EszScJ9fUN5hVHJYOBhWgV0uk0YrEoFEXG6OiIMW632+H3ByGKElvNE61SJpNBJpMxNkL0eDyYmZmBzWaH3++HKIbR1tbO+UQrMLAQnUQ+n8fgYD9kWcbAQB9yuRwAQBAEtLd3QBTD6O7uZqt5olXI5/MYGhqAosjo6+tFZ6cPb3rTmwEANTW1uPrqt6CtrZ3ziU6KgYXoOLquY3JyYumSj4J0OmU8VldXv7RFvojKykoTqyQqD7quY2pq0phPqdSrzQQTiQQ0TTPOonR1+c0qk8oEAwsRgLm5OaiqDEWJIB6PG+MVFRUIhUSIYhj19Q28dZJoFXRdhyAI+PnPf4JoVDXGC00HRYRCIhob2XKCzgwDC21Zi4uLiMV6oCgRjIwMG1vk22w2dHV1QxQLreZ5HZ3o9LLZRcRiMXR1dRstV7Zta0FfXy98vm5IkoT29k62nKA1Y2ChLUXTNAwNDUJVZcRiMeRyWeOx1tY2SFIY3d0BOJ1OE6skKg+api21nIigp6cHuVwWl19+BcLhHQAASdoOUQxzPtG6YGChLeHYsSnIcgTRqIJkMmmM19TULK1LkYw7Fojo1Kanj0GWI1BVecV8qq6uhsXy6hkUNrel9cTAQpvWwkISqqpAUWRMTU0a4y6XC4FACJK0HU1NvI5OdCYWFpJ4+OFvGJdQC/Op0HKiuXkb5xMVDQMLbSrZbBZ9fTEoiozBwQHjTdVqtcLn64IohtHZ6eN1dKJVyGaz6O/vRTw+jQsuuAgAUFHhQUeHD1arxZhPbDlBG4G/ZVT2dF1fuo4uIxaLGq3mgcKiP1GUEAiE4HK5TKySqDzouo7R0REoioyeHhWLi4sQBAHbt+8ybud/85uv4ZkU2nAMLFS24vE4VFWGLEcwPz9njFdVeSFJEkIhCTU1tSZWSFQ+EolZyPJRyLKMubmEMV5ZWQVJCsNieTWgMKyQGRhYqKykUilEoyoUJYKJiXFjnK3mic7O8PAQnnvutwAK8+n4lhOcT1QKGFio5B3far6/vw+apgEALBYLOjo6IYph+HxdbDVPtAr5fN6YT21tHdi16xwAgN8fRF9fL4LBELq6/JxPVHIYWKgk6bqO8fGxpf0doitazTc2Nhpb5LvdFSZWSVQedF3HxMQ4ZDmCnh7VmE9zc3NGYHE6nbj66mvMLJPolBhYqKQkErNQlMIW+bOzs8Z4ZWWl0Wq+rq7exAqJyssLL/wOkcjLmJmZMcY8Ho8xn4jKBQMLmS6TyaCnR4WiyBgdHTHG7XY7/P4ARDGM1tY2bpFPtArZ7OKKjscTE2OYmZmBzWaH3++HKIbR1tbO+URlh4GFTLHcal6WZfT39yKXywEo3H3Q1tYOUQzD7/ez1TzRKmiahsHBAShKBH19fbjxxptRXV0DANi9ey98vm74/QHuPEtljYGFNoyu65icnICqKq9pNV9XV2+sS1ne64GITk7XdUxNTUJRZESjChYWXp1PfX292LPnXABAS0srWlpazSqTaN0wsFDRzc3NIRpVIMsRxOPTxvhyq3lRlNDQ0MhbJ4lWKR6P46c//TGmp48ZYy6X25hPjY1NJlZHVBwMLFQUy63mFSWC4eEhY4t8m82Grq5uiGIY7e0d3CKfaBWy2UXMzc0ZC84rKysxPz8Hm80Gn68boigubZfP+USbFwMLrRtN0zA8PARFiSAWiyGXyxqPtbS0QpLC8PuDbDVPtAqapi21nIggFutBZWUlbrrpFgiCALvdjquvvgb19Q2cT7RlMLDQWZucnMRvfvMCIpGjK1rN19TULK1LkeD1ek2skKh8TE8fg6LIUFUZ8/PzxrimaUilFlBR4QEAtLa2mVUikSkYWGhNFhaSUFUF0aiCZHIWqdQiNE1fajUfgiSF0dTUzHUpRGfgN795Fs8//1vja6fTiWAwBFEMo7l5G+cTbWkMLLRqy63mZVnG0NAANE2DxSLA43HB7w8gEBDZap5olXK5HPr6etHQ0GA06WxpaYXFYoHP14VQSILP18X5RLSEM4FOSdf1pevoMmKxKBYXF43Hmpu3Yfv27bjggr1IpzXkcpqJlRKVPl3XMTo6AlWV0dMTRSaTwZ49e/H6118KAGhra8ehQ+9mywmiE2BgoROKx+NQVRmKsrLVfFWVF6JY2NK7pqYWNpsFbrcb6XTyFM9GtLXNzs5AUSJQFAWJxPEtJ6qMNSlAoaEnwwrRiTGwkCGVSiEaVaGqMsbHx4xxh8OBQCAEUZTQ0tLK6+hEZ0DTNHzve981Nkq02+3GfGptbeN8IlolBpYtLpfLGa3mBwb6kc/nARQ+6bW3d0KSJPh83Ww1T7QK+XweAwP9GBrqx9ve9lYAhbkkihLi8WmIooSuLj/nE9EaMLBsQSdrNQ8AjY2NEMUwgsHQilPVRHRihfk0AUWJIBpVkU6nYLEIOP/8vaiqKmz0tn//xTyTQnSWGFi2kERi1tjf4USt5iUpbOykSbSVWCw9cLu/Dqv1KAA7stn9SKdvga7XnvRnFhaSOHr0FShKZMV8qqioQDi8HV6vF0sbPJdxWMnAZnsBgpCCpnUgnw+aXRBtYQwsm1wmk0EsFoUsRzA6OmKM22x2BAIBiGIYra1tbDVPW1QOlZV/A7f7S9B1K4A8AAEOxw/h8dyFubn/F5nMO0/4k4lEAr/5zbMACvPJ7/cjFJLQ3t4Bh8OGmhoP4vFyXYyeQkXFP8Lt/hIslhljNJu9EMnkx5HNXmFeabRlMbBsQvl8HkNDA1AUGX19vcjlcgAKn/La2tohimH4/X7Y7Ww1T1tbZeXfwOX6MgBAEPJLo4XTIrqegdf7XzAz40Q0egFUVYbbXYGLLy7cgtzcvA2SFEZrazv8/gAcjs0yn1KoqflT2Gy/hSCs3KrAZnsO1dV/jrm5e5HJvMOk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/dMLHdB2YnLTg5ZftOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9TXouvW4MysFf/iDDc8/78DU1HLn43lUVg6hu/sahELSJu+InILL9eUThpVlglAIdG73V5BM/t3GlUZbHgNLGcpmFxGLxaAoEQwPD0FfWtlns9nQ1dUNUZTQ3t65yd9Yic6O1foKgDwWFwGbDVhexhWPWzA1ZYXFoiMQyGHHDg2NjS1YXLzM1Ho3gt3+a1gsc6f9PkHIw+H4AQMLbSgGljKhaRqGh4eWWs3HkMtljcdaWlohSWH4/UG2midaBV3XMTioQ1VdUFUb3vzmFILBwpmWXbuyqK7WEQpl4XYDum5BMrk19k0RhIXTf5PxveW6oJjKVdEDS39/P7785S/jyJEjUFUVfr8fP/zhD4v9spvG9PQxyHIEqiojmXz1DaKmpgahUGGLfK+32sQKicrH9PQ0FCUCVVWQyVTAbrdDEIC+PpsRWGprddTWvvqBQBA0ZLP7zSp5Q2la26q+T9ct0LSOIldDtFLRA4uqqnjyySexZ88eaJpmXL6gk1tYSEJVFaiqjMnJSWPc5XIhEAhBksJoamrmuhSiVcpkMviP//h3TE5OGGNO5/nYs+cx7NiRQmvridds6LoVudwu5HKv26hSTZXL7UYuF4bVKkMQTvVerSGV+ouNKosIwAYElgMHDuDgwYMAgDvuuAN/+MMfiv2SZSmbzaK/vxeyLGNoaACaVngDtVqt6Oz0QRTD6Oz0sdU80SrkcjkcOzaF5uZtAACn0wld12GxWIz55PN1obKyBlVVf4UTfY4q7MviwPz8PRtbvKkEJJN/i+rqQyf9Dl23QtPakMn8+QbWRbQBgYUbkp2crusYGRmGqiro6VGxuLhoPNbcvA2iGEYgEITb7TaxSqLyoOs6xsZGoSgR9PREkc9reNe73mPsj/LGNx5AZWXlim7I6fRfQNfd8HjuhNU6vhRSdAiChnx+O+bmvoBcbq85/0ImWVz8M8zP3w2P5+MALMZdVLouANChaa2YmfkPAOwqTRurrD+uW63lGYZmZuKQ5QgURUYikTDGa2qqIYqFLfJrak6+JXgpWT4G5XosNpOteixmZ2cgyxHIsoxEYtYYr6qqQjKZQEVFEwCgpWXbCX8+n387EokbYLf/FBZLYWv+XO71yOdfB0DAWk5qlvuxyGZvQyJxAE7nF+Fw/AhACprWiUzmvVhcvAkWiwfl8lm03I/FZnM2KxnKOrB4veVz5iGVSkGWZbzyyisYHR01xqurKyFJErZv34729vayXZdSTsdis9tKx+IPf/gDfvKTnxhfe70eiKKIHTt2oKOj4wzn0w3rXl95H4sLlv5XYLEUbv/2lGlP1PI+FgSUeWBJJFLI50++wZHZcrkc+vv7IMsRDAz0I58vnFq1WCzo6OiEJIXR1dVttJqfmVn9LYWlwmq1wOt1l/yx2Ao2+7HI5/Po7++Dy+VCa2vhbhavtwGZTA7t7R2QpDC6u/0lMZ82+7EoJzwWpaW62r3mpSJlHVjyeQ25XGn9AhZazY9DliPo6VGRTqeNxxobGyGKYQSDIVRUvPoxpdT+HdaiFI/FVrWZjkVhPk1AUSKIRlWk0yl0dvrwlre0AADcbg9uvfU9cLlcxs+U0r/7ZjoW5Y7HojSczY3CZR1YSkkikYCqyq9pNe/xeIz9UurrG8wrkKiMJBIJRKMKFCWCeDxujFdUVKCurh66rhuXe44PK0S0eTGwnIVMJoNYLApZjmB0dMQYX241L4phtLW1804pojP0+OM/M+aUzWZHd3c3RDGM9vYOzieiLarogSWVSuHJJ58EAAwPD2N+fh6HDx8GAFx44YWoq6srdgnrStM0DA4OQFEi6OvrRS6XAwAIgoC2tnaIYhh+vx92+2ZpNU9UPJqmYWhoEKoq4/Wvv9S4hV+SwrBYLEvzKWDcmkxEW5egF3nr2aGhIVxxxRUnfOzrX/869u3bt+bnjseTG3JNUtd1TE1NQlFkqKqCVOrVxXy1tXWQpDBCIQmVlZVFr6XU2GwW1NZ6NuxY0MmV07EozKfCFvkLC4X5dMklb8A55+wGgBWXfMpROR2LzY7HorTU1XnWfIt50c+wtLe3Q5blYr9MUczPz0NVC9fRp6ePGeNudwVCIRGiKKGhobGs31iJNkomk8HRoy9DUWQcOzZljLtcbgSDIbS2thpjnFNE9Me4huWPZLOLiMViUFUZQ0ODRu8jm80Gn68bkiShvb0TVqvV5EqJSt/xZ0o0LY9f//pX0DQNVqvVmE8dHT7OJyI6LQYWFK6jj4wMQ1EiiMV6kM2+2qm1paUVoighEAjB6XSaWCVReVhuOSHLEaRSKbzlLW8FUDgzuXfveaisrEQgENpUd/cIwjSczu/Aah2ArjuRz/8JgBNfCieitdnSgWV6+tjSuhQZ8/Pzxnh1dTVEMQxRlOD1VptYIVH5mJ6eXrq1X8b8/JwxnkjMGvNo3779ZpVXJHl4PJ+B230PgCwKb6k6BOEfAeyCxfIAgLCpFRJtFlsusKRSC8a6lMnJSWPc5XIhEAhCFMNobt7Ga+hEq9TXF8Nzz/0Wk5MTxpjT6TTmU1WV18Tqiquy8r/B5XoAgrB870L2uEePoqrqIGZmnkA+HzKjPKJNZUsElmw2i/7+PihKBIODA9C0wkpxi8UCn68LohhGZ6cPtrV0OSPaYnK5HHRdM27dz2azmJycgMViQWenD6GQhK6ubpPmUx52+y9gtQ5C191YXLwcut5UlFey2Z6H2/3lU9YiCEl4PP8dicTDRamBaCvZtH+hdV3H6OgIFEVGT4+KxcVF47GmpmZji/zlfR+I6OR0XcfY2CgUJYKenijOPfd1OPfc1wEAurr8uOSSy5bmU4VpNbpcX0NFxf+A1TpmjOm6FZnM2zA//4/Q9fp1fr0vQ9dtEITcSb9HEPJwOH4Ci2UImta+rq9PtNVsusAyMxOHqsqQZRlzcwljvLKyytgvpba21sQKicrH7OwMFKWwLiWRmDXGh4YGjcBit9txzjl7zCoRAFBR8Tl4PJ95TZ8SQcjD6fwebLYXMDPz83UNLXb7r04ZVl6tQYfN9gIWF7dqYCmENofj5wDS0LQupNPvYICjM7YpAks6nUY0qkJVZYyNjRrjDocDfn8QoiihtbWN61KIVknXdfzoRz/A4OCAMWa32+H3ByFJYaNbcimwWl+Gx/MZAMCJprgg5GG19sHj+TTm5/+/dXzlM9lzs6j7c5Ysm+3X8HrfBat1BLq+/OdGR0XF/0A6/W7Mz/8jALuZJVIZKdvAous6YrEeHD16FP39fcjn8wAKG051dPggiiK6ul5tNU9EJ5fP5zE2Noq2tsKnXkEQ4HK5luZTJ0SxsC6lFFtOuN1fWtWlGZfr35BM3gVdX587/3K5c2G1DqzqLEsut2tdXrOc2GxHUFPzViwvRP7j/04u11cgCHOYm/sSAH6YpNMr28CSSCTw4x//CJpW+OTS0NAIUZQQDIbg8Wy9LfKJzpSu65icnDBaTqTTKdx00ztQV1e4bHLhhRdh//6LS34+ORw/X+WlmTRstueRzR5Yl9dNp98Hl+u7p/weXbcim70UmuZfl9csJx7PnQCyEIT8CR8XBB0u17eRSn0AudzaW7TQ1lG2gUXXdXg8Hvj9IUhSGPX1DWaXRFQW5ubmlvZLiSAejxvjFRUVmJtLGIGlfPYgyqz6OwVh9d97Otns65FOvw1O5/chCCfqUWMBYEcy+el1e81yYbH0wOF48rTfp+s2uN1fwtwcAwudXtkGFo/Hg0OH3g2NvayIVm10dATf//4jK1pOdHf7EQpJ6OjohMWytqZkZsrnRVgskyf9JL/ye4Pr+MoC5ua+CF2vgsv1IAoBpTBeOOPTjLm5B5HLmbsg2Qw220ur+j5ByMFme77I1dBmUbaBxW63w2JZNPZUIaKVNE3D0NAgstksAoHCH+qmpma4XG7U1tZCksLw+4NwOEpvXcqZSKffA4fjl6f8Hl23IJe7sAgbuDkwP/8vWFj4v+FyPQSrtR+67kY+fyUqK69HPp8BsBXfo85kTQrXr9DqlG1gIaITm5qawiuvvAJVlbGwsICqKi/8/gAEQYDVasU73nFr2YeU42Uyb0U2uwc22x9OeJZF1wUAApLJTxStBk3zYWHhb42vbTYLCm+v63cJqpzkcnuh68JxOwCfWGGNDy8H0eowsBBtAsnkPGKxKAYHYxgcHDEWo7tcbvh8XcjlssYdPpsprBTYMTv776iuvh52+/PQdSsEIb+0J4sAwIFE4gFks5eaXOfWoWk+LC4ehMPx+Ckv1QlCHun0+zawMipnDCxEm8Bzz/0GkcgrcLsdsFqt6OrqgiRJ6OjwwWq1ml1e0el6PWZmHoPd/jhcrgdhtfZC1yuwuPhmpNO3rPsut3R6yeT/hN1+OYDUSc98pdN/gVzuvI0vjsoSAwtRGdF1HSMjw1AUGTt27EJzczMAQBTDmJmJ44ILzkVTUztsts12FmU1LMhmDyKbPWh2IQQgn5cwM/MTeL1/AZtNXdo4TgCQB2BFKnUbkslPmlwllRMGFqIyEI/HoSgRKIqM+fk5AIDVajUCS0tLK6677gbU1noQjyeRy23FhZ5UavL5cxCPPwe7/Wk4HI8BSEHTfEinb+RZLzpjDCxEJSqXy+Ho0VegKBFMTIwb406nE4FAYYt8otInIJu9BNnsJWYXQmWOgYWohOi6bvS8slqtePHF5zE/Pw+LxbK0RX4YXV3dsNk4dYloa+G7HpHJdF3H+PgYFCWCkZFh3HjjO2CxWCAIAs4993xoWh6hkAi3u8LsUomITMPAQmSSRGIWshyBqsqYnZ01xoeGBtHZ6QMA7Np1jlnlERGVFAYWog02NjaKZ599BqOjI8aY3W6H3x+AKIbR2tpmYnVERKWJgYWoyPL5PBYXF+F2uwEU+veMjo5AEAS0t3dAFMPo7u42NnYjIqLXYmAhKgJd1zE5OQFFkaGqCnw+Hw4cuBIAUF/fgDe84XJ0dnahsrLS5EqJiMoDAwvROpqbm4OqylAUGfH4tDE+NjYKTdOMxbQ7duwysUoiovLDwEK0Tn7xi8cRibwCvdDEBjabDV1d3RDFMDo6OmGxWEyukIiofDGwEK2BpmkYHh7Ctm0tsNvtAICqqirouo7W1jZIUhjd3QE4nU6TKyUi2hwYWIjOwLFjU5DlCKJRBclkEldeeRWCwRAAYMeOXQiFJHi9XpOrJCLafBhYiE5jYSEJVVWgKDKmpiaNcZfLjcXFjPG12+027gQiIqL1xcBCdArJ5Dz+9V+/Bk0rNBO0Wq3w+bogimF0dvpgtVpNrpCIaGtgYCFaous6RkaGMT09jXPO2Q0A8Hgq0djYBEEQIIoSAoEQXC6XyZUSEW09DCy05cXjcShKBIoiY35+DlarFaGQaASTt771z4yFtUREZA4GFtqSUqkUolEVihLBxMS4Me5wOBAIhJDL5YwxhhUiIvMxsNCWpKoynn76KQCAxWJBR0cnRDEMn6+LAYWIqAQxsNCmpus6xsfHoCgRtLa2IRgUAQDBYAiqqiAUEhEMhlBR4TG5UiIiOhUGFtqUEolZKIoMRYlgdnYWQGGtynJgqajw4LrrbjSzRCIiOgMMLLSpRCKvIBI5itHREWPMbrfD7w9AFMMmVkZERGeDgYXKmq7rEATB+FpVFYyOjkAQBLS3d0AUw+ju7obd7jCxSiIiOlsMLFR2dF3H5OQEVFVBNKrg+utvgsdTCQDYvXsP2ts7EQqJqKysNLlSIiJaLxsSWHp6evCZz3wGL7zwAjweD6699lp85CMfgcPBT720enNzc4hGFchyBPH4tDHe0xPF7t17AQA+Xzd8vm6TKiQiomIpemCZnZ3Fu971LnR1deGee+7B+Pg47r77bqTTaXzyk58s9svTJjAzE8dTTz2J4eEh6LoOALDZbOjq6oYohtHe3mFyhUREVGxFDyzf/OY3kUwm8S//8i+oqakBAOTzedx111344Ac/iObm5mKXQGVG0zQsLCwYl3Tc7gqMjY1C13W0tLRCksLw+4NwOp0mV0pERBul6IHll7/8Jfbv32+EFQC4+uqr8alPfQpPP/00/vzP/7zYJVCZmJ4+BlmOQFVlVFR4cP31NwEAnE4nDhy4Eo2NjfB6q02ukoiIzFD0wBKLxXDdddetGPN6vWhsbEQsFjur57ZaLWf183T2lo/BWo/FwkISilJYlzI1NWmM67qGxcU0KioqAACSJJ59sZvc2R4LWj88FqWDx6K0HHdT5xkremBJJBLwer2vGa+urjY29Forr9d9Vj9P62ctx+LZZ5/Fr371K2iaBgDweFzw+/3YsWMHuru7YbPxJra14LwoHTwWpYPHovyV9V+ERCKFfF4zu4wtzWq1wOt1n/ZY6LqOkZFhVFfXGGtTbDY3ksk0mpu3QZLCCAZDcLsLbypzcxkAmY34V9g0VnssqPh4LEoHj0Vpqa52w2JZ29muogcWr9eLubm514zPzs6iuvrs1iPk8xpyOf4CloKTHYt4PA5VlaEoMubmEjj//AtxwQX7AABtbZ246aZbUFNTa3w/j+fZ47woHTwWpYPHojQs3ei5JkUPLH6//zVrVebm5jA5OQm/31/slycTpFIp9PREoSgRjI+PGeOFfXde/W212WwrwgoREdHJFD2wXHbZZbjvvvtWrGU5fPgwLBYLLr744mK/PG0wTdPwzW8+hHQ6BQCwWCzo6OiEKIbh83XBbrebXCEREZWjogeWm2++GQ8++CA+/OEP44Mf/CDGx8fxuc99DjfffDP3YClzuq5jdHQUr7wyju3b9wAoBJTubj+mpiYgioV1KRUVHpMrJSKiclf0wFJdXY2vfe1r+PSnP40Pf/jD8Hg8uP7663H77bcX+6WpSBKJWSiKDEWJYG4uAbfbgYaGFtTVNQIALrnkMt7hQ0RE62pD/qoEAgF89atf3YiXoiLJZDKIxaKQ5QhGR0eMcYfDgR07dsBme/VSD8MKERGtN/5loVWZmBjHL37xOABAEAS0tbVDFMMQxSCammoRjye5Ap+IiIqGgYVW0HUdU1OTUBQZbrcb5513PgCgra0d7e0daG/vRCgkHreXCnePJCKi4mNgIQDA/Pw8VFWGLEcQj08DACoqKrB373mwWCywWCx461v/zNwiiYhoy2Jg2eJisR68/PJLGB4egr60o4/NZoPP1w1JkkyujoiIqICBZYvRNA2CIEBY6kA1MjKMoaFBAEBLSyskKQy/Pwin02lmmURERCswsGwR09PHIMsRqKqMK674E7S1tQMAtm/fAZfLBVGU4PWeXasEIiKiYmFg2cQWFpKIRlUoSgSTk5PGeE+PagSW+voG1Nc3mFUiERHRqjCwbELpdBqPPfYzDA0NQNMKtxpbrVZ0dvogimF0dvpMrpCIiOjMMLBsArquI5GYRXV1DQDA6XRidjYOTdPQ3LwNoZCEYDAEt9ttbqFERERrxMBSxmZm4ktb5MtYXMzg0KH3wGazQRAEvOENB1BR4UFtLbshExFR+WNgKTOpVAo9PVEoSgTj42PGuMPhwPT0MTQ1FRpKLq9RISIi2gwYWMqIosj4xS8eQz6fB1DojNze3glJkuDzdcNut5/mGYiIiMoTA0uJ0nUdExPjsFqtaGgodEFubGxCPp9HY2MjRDGMYDCEigqPyZUSEREVHwNLiUkkElBVGYoSwczMDAKBEP7kT64CANTW1uLtb38namq4LoWIiLYWBpYSkMlkEItFIcsRjI6OGOM2mx0OhwO6rhs70zKsEBHRVsTAUgJ+9KMfGAtoBUFAW1s7RDEMv98Pu91hcnVERETmY2DZQLquY2pqEqoq44IL9hlhxO8PYHFxEZIURigkobKy0uRKiYiISgsDywaYn5+HqipQlAimp48BAOrrGyFJYQDA7t17sWfPucZlHyIiIlqJgaVIstksYrEeKEoEw8ND0HUdAGCz2eDzdaO6+tVGgxaLxawyiYiIygIDS5Gk02k8/vjPjK9bWlohihICgRCcTqeJlREREZUfBpZ1MD19DLIcQSaTwRvfeAAAUFVVhXB4B6qqqiCKErze6tM8CxEREZ0MA8sapVILxrqUyclJAIVLOxdeuM/YzO3yy68ws0QiIqJNg4HlDA0PD+HIkRcwODgATdMAAFarFZ2dPohiGA4HL/cQERGtNwaW09B1HZqmwWq1Aih0SO7v7wMANDU1G1vku91uE6skIiLa3BhYTmJmJg5VlSHLMvbuPQ+7dp0DAAgEQkgmkwiFJNTWctdZIiKijcDAcpx0Oo1oVIWiRIydZwEgFosagcXlcuHCCy8yq0QiIqItiYEFhcs+P//5T9DbG0M+nwdQWEDb3t4JSZLg83WbXCEREdHWtiUDi67riMenUVdXD6DQvyebzSKfz6OhoRGiKCEUEo27fYiIiMhcWyqwJBIJqKoMRYlgZmYGt9xyyNgf5cILL8K+fftRX99gcpVERET0xzZ9YMlkMojFopDlCEZHR4xxm82OqakpI7A0NDSaVSIRERGdxqYOLKOjI/jhD7+PXC4HoHDpp7W1DaIYht8fgMPhMLlCIiIiWo1NE1h0XcfU1CQWFxfR1tYOAGhoaIAgCKitrYMkhREMiqiqqjK5UiIiIjpTZR9Yksl5KEphi/zp6WOora3DTTe9A4IgwG534KabbkFlZSUEQTC7VCIiIlqjsg0si4uL+MEP/h0DAwPQdR0AYLPZUFdXj1wuC7u9cLmHZ1SIiIjKX9kGloWFBQwOFsJKS0srRFFCIBCC08lePkRERJtN2QaWQmfkixAIhIw7fYiIiGhzKtvAUlVVhQsuuBC5nGZ2KURERFRkFrMLWCsuoiUiIto6yjawEBER0dbBwEJEREQlr+iB5emnn8ZHP/pRHDx4EJIk4e///u+L/ZJERES0yRQ9sDz11FOIRCK44IIL4PV6i/1yREREtAkV/S6hv/mbv8Edd9wBAPj1r39d7JcjIiKiTajoZ1gsFi6TISIiorNTtvuwAEB1tRtLu/KTSZbvLuexMB+PRengsSgdPBalxWJZ+5YkZR1YePamdPBYlA4ei9LBY1E6eCzK3xkHlrm5OUxMTJz2+zo6OuBwONZUFBEREdHxzjiwHD58GHfeeedpv+/RRx9FIBBYU1FERERExzvjwHLDDTfghhtuKEYtRERERCfEi3pERERU8oq+6HZ4eBgvvfQSACCVSmFgYACHDx8GAFx11VXFfnkiIiLaBARdL+6NXo888gg+/vGPn/AxWZaL+dJERES0SRQ9sBARERGdLa5hISIiopLHwEJEREQlj4GFiIiISh4DCxEREZU8BhYiIiIqeQwsREREVPLKPrA8/fTT+OhHP4qDBw9CkiT8/d//vdklbQk9PT1497vfjb179+Liiy/G5z73OSwuLppd1pbU39+PT37yk7j22muxY8cOXHPNNWaXtCX9+Mc/xl/+5V/isssuw969e3HttdfiO9/5DrhzhDmefPJJvPOd78RFF12EXbt24YorrsBnP/tZzM3NmV3alpdMJnHZZZdBkiRjY9nVKPpOt8X21FNPIRKJ4IILLsDs7KzZ5WwJs7OzeNe73oWuri7cc889GB8fx9133410Oo1PfvKTZpe35aiqiieffBJ79uyBpmn8A2mSr371q2hra8Mdd9yB2tpaPPPMM/jEJz6BsbEx3HbbbWaXt+XMzMxg9+7duPXWW1FTUwNVVXHPPfdAVVU88MADZpe3pX3hC19APp8/8x/Uy1w+nzf++fLLL9fvuusuE6vZGu677z597969ejweN8a++c1v6tu3b9fHxsbMK2yLOn4OfOxjH9Pf8pa3mFjN1nXs2LHXjN155536eeedt+IYkXm+9a1v6aIo8n3KRNFoVN+7d6/+jW98QxdFUf/973+/6p8t+0tCFkvZ/yuUnV/+8pfYv38/ampqjLGrr74amqbh6aefNq+wLYpzoDTU1dW9Zmz79u2Yn5/HwsKCCRXRH1t+z8pms+YWsoV95jOfwc0334zu7u4z/lm+09EZi8Vi8Pv9K8a8Xi8aGxsRi8VMqoqo9Dz//PNobm5GZWWl2aVsWfl8HplMBi+//DI+//nP48CBA2hvbze7rC3p8OHDUBQFH/7wh9f082W/hoU2XiKRgNfrfc14dXU11xERLXnuuefw6KOP4mMf+5jZpWxpl19+OcbHxwEAl156Kf7X//pfJle0NaVSKdx99924/fbb1xzgSy6wzM3NYWJi4rTf19HRAYfDsQEVERGdmbGxMdx+++3Yt28fDh06ZHY5W9r999+PVCqFaDSKe++9Fx/60Ifwla98BVar1ezStpR7770X9fX1uO6669b8HCUXWA4fPow777zztN/36KOPIhAIbEBF9Me8Xu8Jbw2cnZ1FdXW1CRURlY5EIoH3v//9qKmpwT333MM1RiYLh8MAgHPPPRfnnHMOrr32WvzsZz/DVVddZXJlW8fw8DAeeOABfP7znzf+diyv61pYWEAymYTH4znt85RcYLnhhhtwww03mF0GnYLf73/NWpW5uTlMTk6+Zm0L0VaSTqfxwQ9+EHNzc/jWt76Fqqoqs0ui40iSBLvdjoGBAbNL2VKGhoaQzWbxgQ984DWPHTp0CHv27MHDDz982ucpucBCpe+yyy7Dfffdt2Ity+HDh2GxWHDxxRebXB2ROXK5HD7ykY8gFovhoYceQnNzs9kl0R85cuQIstksF91usO3bt+PrX//6irGjR4/is5/9LO666y6cc845q3qesg8sw8PDxk55qVQKAwMDOHz4MADwlF+R3HzzzXjwwQfx4Q9/GB/84AcxPj6Oz33uc7j55pv5Jm2CVCqFJ598EkBhPszPzxtz4MILLzzh7ba0/u666y488cQTuOOOOzA/P48XX3zReGzHjh1cc7fBbrvtNuzatQuSJMHlciESieDLX/4yJEnCwYMHzS5vS/F6vdi3b98JH9u5cyd27ty5qucRdL28t8V85JFH8PGPf/yEj8myvMHVbB09PT349Kc/jRdeeAEejwfXXnstbr/9dr4pm2BoaAhXXHHFCR/7+te/ftI3ClpfBw4cwPDw8Akfe+yxx/ipfoPdf//9ePTRRzEwMABd19HW1oYrr7wS733ve3mbeQn49a9/jUOHDuE73/nOqs+wlH1gISIios2Py9eJiIio5DGwEBERUcljYCEiIqKSx8BCREREJY+BhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUcljYCEiIqKS9/8DQLeYnUD1XxEAAAAASUVORK5CYII="}}]}}, 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IwEEphVf+u0ulMoHAIaBhgoK0ATNZe65UcgiFzI615yajePQxuFtt5X9Bjy9KlT0V0yR/9qcnffxMBQ6dTm41/3IflErVfTGUB57rCzYKhZHVohCAgny+befSGPUFDslkonLNEAgcAqZFUJA2UGqp56QUCCHKrbf2t+cmJRYjddMtuNts4xefUsn/ci7a4DioSJTUT3+Gs/sbah7C/3L0iMUiDT/tbCS3ak88QeysT9W+g1L+qgnA81DRKKqvr23n0wj1IipKJQcpJfl8gUwmF0RUBEyLwKlhA2O8ei6dzk1QzxWLJSzLKDsvjFXP+a4KikzGv0qvDEm2OrnV23RThv5wH+bvf4f1q5vQli/HSySwDz2M4rHHoZLJKY8xHQeH0eq5dDrbMceH8KUXIwoFX7hQa95IKYTjoKSkeMKJ9dWHHaKWg4MfL+8QDocCB4eAaRPIvjcQ6nnPhUIjSqnR3nNK+QVLSlmVbU/mPZfNZme2ImojUzk4NOs91wrE6tUk3/xGv02n64hMpraYQwhIJhm85494ixd35PymYrSDw+BgqlyQYO3awcDBoUsJZN8Bs04zw62V+aFoNNTe9lybmUzgIAYHMR55mPi/H6PXLnSsPTcebcVyhOP4y1YpUZFIeZ+oBnffTWiH13Xs/KZitMChtzeO53nlfcnAwSGgOYKW3XpMI+25dDoH+K238cOtAFJKstlCefZkVHsuk0X+97/o6Qxqfj/uNtvW/zKdJSoCh0gkTPHFJViXXYJ1112IfA6UwguFcQ46CO9jn4Att+zouSnDHBl+BdB1VDwOto0olUYMZYVAzZtHcZddCYdDFAp2VcU22/hxGf7fe6FQBATRaGRMu25oKE1/f29V4NDfnyQSMcnn3WCVFDCGoGW3HtJMe67ivDC+PWeaOkIIpPRdvLPpNMbVVxO66iq0l5f5X5pS4uy0E/mPfxL73YfP8qufnN6hdWjvPRqWLQPTRIXDKKUQ+QIUi3gLFpD+2TW42+/QuZOybZJ7vhn52ipUuEZirFII26b4nqPJXHIZyWQC13W7IqIiHLaIRMLlfaMShmFOy8Fh7dogoqLTBC27gI7Sau85WXYMKBQKpIaGCZ19NtEvfwlt6RKUpqFMEyUE+n/+Q/yjHx5jeNotCECe9SnEsmWQTOLFYiipgaajYjFUMolcvZrYZz7dWb8406Rwyqn+FUSt57VtEILCaacDvl2RaZqY5uwJGwxDp7c3QSQSplAoMjg4TDqdbdjBIZvN4Xke8XiEwMEhYDRBQVpPaGdya7FoY5omoZtvJnTzr/xCFA77rgJlB24VCqGAyLe+6Q95dgmhkEXylaWIf/wDFY36v6jxSImKRdGefx7joQc7en75j36M0pve7LfoisXyHJIfsyHyeYRS5D/+CZw93gSAbZew7VI1BLGTSCmIx6P09MRRymNoKEU2m6/OHDXq4GBZFqlUllDIChwcAsYQFKQ5Tie857LZPChF5Oqf+k9aS3ZsmuB5hH5+bYdefW1GD7c6f/gjyrbxTN/fbtIWkeGfu/HXBzp7ouEwqet/Sf79H4BwBFEs+oWoVMJdtCmZb36L3Oc+P+YhmYyvfOxk1EY4bJFM9mAYetm1IlMWs/gEERUBrSAQNcxhOpLcikApRe7Z54k98zSeVuctU96AN3/32/a/+JqnMDG51RgaRhf+5rtSvuGnEmqsWZwoWxvNhgtCNEruvK+T/+z/YPztb4hsFm/BAkp77jVpBIf/xV7oiMDBMHSiUf/9VCgUR7lvTIbv4JBM9oxxcAgEDgGNEhSkOchM1XPNDLeWVq5ElUr+l4bnonR98vaXEIiKD1ubc4nGU2u4VVu40LfjcV0UGkLzRRreqCt836JI4U3ijdcpVDyBffAhDd03lytgWSaxWLgtAgcpfbWcZZmUSiWGhsauiCZjvINDRcAQj0dJpbL09fmFKpPJl41XC1UHh0IhEDgEBC27OcWsREMoRejqn5I4+cQRC59CAZHJ+IOc4zbjhevi9fd3tBhN5T1nH3wIKhZHZP0vbs/zEKWSL/3O58ApIXI5sCzsdx/RsfOeKe0SOEzVnqvHdCMqAoFDwGiCFdIcoVPtufGEL7uUyHcv9HetDcMvShVcF5HNoqIx0GR1bqZw0kkd+I1M3p6bzI1bJZMUTjuN8OU/hFQKUSygikWEUgjP8yu9plE86j14m27akXNvBaMFDnYjGVJTML32XG0qAoeenjj5fLEqcFi3LkV/f7IqcOjr66kKHJLJRHkUQQWrpA2YYIXU5bRTPTdVNIR8+WXCF30fBb6p53gngUpIXiEPnocoFvGSSYonn9L238t0oyHyZ30a+8CDkMND/mqosu9SeT1Soj/xb7T//Kft595KWiFwqKeea4ZA4BDQLEFB6lK6Ibk1dOMvfTlyZWjTMFCxmL9PBCMOA47jF6O+PtLX/QJv4cZt+700HQ0hBHLNGlQsjupNgmX5qsDeXtwttsDdehvkwDqiX/lS7RymLmS0wEE2EW0+k/ZcfepHVCQSsbIkPIioCBghaNl1IbPVnhuP/s9/VttwVSzLFzQUiyNzM0DxyKPIfv0bqHnzWv3r8M+2wfZcLfRH/4H27LN48+ePFFhA0zQ0FK7r4SWTaC+8gP7ggzj77NOOl9EWmhE4tKo9N4HKDJWuBwKHgGkTrJC6iNlsz01KrZWCpvntu74+P5vHNCnt/46JxahYxLz7LuLvO43evd5M7757ET37Uxh/++vUEeWjaEVyq/6vf/rZSqGxrS3fEFQgpPB/5roY//rntI7dDTQqcGh1e66C/tBDxN9/BvM23Zj+jTeib7utiX7tPArPPR8IHAIaJihIXUA3tOcmw9l557Hmn5Odu+OAruNsN9aFWgwPE//QB4idczbG3/+GSKcRg4NYv7mb+Ac/QOSrX5nSpqeVya2i6FvwjDeAVUrhKTW23VWauUCg0zTi4NCu9lz44ovoPfxQzLvvQpQzneTgIOEfXU7vvnuTv/cPgYNDQEMEBWmWabX3XCujIYrHn4DStJHE0nEoz/ONSXffHXeXXUb9QBH9/OcwHvw7XjyON38+qqcH1duL19+PMg1CN/6S8I8vn/S47Uhu9TbdFKEUuM7En5VXglIphFJ4m2zS9PPMJrUEDpN5z7UqgNC88/+Inv81wJf8j0aU7Y8ix74Xe9myQOAQMCVBQZoluq49Nwnu9jtQPPkU/4smnx+7UnJdyGZR4TDat76FPspOSHvySYwH7ve94yxr7EGF8BV7moZ13XWITBpyOcy77iT6+c/R84mP0ffN8zEf/DvpoVRT7bnJsN91AF5fEjE0PPGHqty6Gx5C9fZiH3jQjJ9vNhgvcGhXe2404Yt+gKojphCeh8jlcH58RSBwCJiSQNTQYepFQ1TaLdlsYUI0hF+IfGFDreTWVGp0cmtrNoSzX/0ayrIIXXONL5fGd9wRSuFttIDMDy4i8ua3EBMwNORHlJi/+w3CtvESiZrHVfE4cnAd1rXXYv3mLrRXXvFXMFKiXBd1442Yu+2O/e3voBYsmPHrUD09FN53JuGLfoAYGkT19I6075SCdUP+HtOHP+zvi81RKgKHRCKGpsly6m97Itnliy9iPPHvKe8nPA/rF9eT//KXA4FDQF2CPKQOYpqSWCxcVcpV1HORiDVBPTd683e8ei6X89VzoZCJ53lks7lpqeeaQb66Auu2W5HLloFuUNprL+yDDgbLQtc1enriZLN5CoUi0c/9D9btt+JttFH9Y65ejYpGka6LWLAAzzDwPM+/gs/nkesGcHbdjfSVP4VodOYvwvMIX3YpoWuvQeSyfvFDIDwXFYlQOu10Qud9lXQ235Yv8E5gGHo1kr1YtMlksi1dEY1Gf/BBeo84rKH7qnCYgZeXk0z2Yts2xWKJ3t4469YNEw6HME2DtWsH6evrQSnF4OAw/f1JSiWHVCqPEJ21oVpf6fY8pGCF1AFGe88BOI5LPm+31Xuu1XibLCL/iU9N+jPHcSkUbD+VtWijolNEcYM/SFso+E4J226LqxjrLRcO483fCP0/T2De83vs9xw98xchJflPnUXx3Ydj3fl/6E/+B5TC2Wlnikccgbf1NoiSU3U+mEvtovHec57noeta24oR+KvOhu8bTwQODgFTEhSkNjJZe85xXKLRUHWfqJvaczMhl8tX247Ft72d0A03+L535ciH0QghkNksOA5q/nw8TzHp92b5sdbtt7WmIJXxttqK/FlnT/qzbNZ3q45EQn7sxhxgdHJrpT0npSSZTBCJhMjlCm15Xnf77XEXb4F8eZnfbq2B0jSK73nPBAeHoaE08+b1VgUOsVikfJHlCxzWrh2sChwGBlIIEXxdre8EooY2UUs9Fw77xcnz/A+wYWgdV8+1A6UU2WyeUMhC7f8O3K23Qg4NjZtl8l22tYogIhTCjcUmL0aV44bCaEuXtPnsR/A8RS7nvw6tw27l06Weem6mDg4NISX5j3+ifjEq/zd/xvvL/xc4OATUJihILaZR9ZyvdlLlQUb/yrZT6rl2USzalEoOsd44me9fhLfxxsi1axHDw0i7hFayEevW4a1bR2n7HVC9veBN8SXjeahagYBtwv9y9IjFIh193kZpVD2XyxXwPI9YrH3psoXT30fhRN9Md7zaTmkaSEn6Rz/G23prYGJERS7nt5wrAgfTNKoCh0gkhBCiKnAQwg2K0npOUJBaRDPDrZ6nyGb9dkoiEW3LcGunyWb9q19j111JXX8D9mmnI6NRP+ohl8Pt7yf/iU+Rvu4XuIsXI1Op2gdTClEo4Oy1d+deQJlsNodh6FjWxJbjbDLd4dZ2RVRUkZLMxZeSvvhS3NeNDEcrIbAPOJDhu36D/Z73jnlIEFERUItAZdcCannPTUc953keUkpKJQdd1zqmnmsHlc1127Z977m1A+SffQ5XKdwttqza91jXXUvkwm/j9SbH+MtVEMNDCMchfcWVOHu8qdMvg3jcv5AYHEzN+pX5TLznKhLwwcE6xb8VKIV8dQUik8HbaAEqmaxzV7870NPjK+0qQp2KwCGfL1As2vT19TA0lEYpRTKZYHAwRalUI4Y+YEq6XWUXFKQZMD65NZcrTEhurWwoRyKhCeo5X7atyOeL1fsIAa7rMTyc6oh6rh2EQlZ1piqTydWWUBcKxM45G/P+v+BZIV+1pWlg2/7+E4rCGWeSP+vTU6v22oCUgmSyh0KhOGsCh/HquUwmN227n4rAIZ8vtE3g0AxKefT0xJFSVgUOmcyI2nRgYIhoNFKVhPf2xtE0jYGBFFIGAodmCArSeki94dZQaGQFNF49l8sVkFISDls11XO2bROL+RYrrQhdAxCrVmHdfiv6v/6FcBzc7bencMyxeFtv05LjV9B1rToDUyo5mKbB0FAax5lo11MllyP8kx9j3X4bYt06hOehdB1v000pnHIaxeNPgHZtyjdARcE2NJTGncJ7r13PXRGMzGQ2KhIJEQ6HGBxMVa2SZhulFJomSSZ7SKezZUuhUHkeKYHnKYaG0vT395LP+6vC/v4kmUyOfN4NVklNEBSk9YxWtOemGm5NJOIta7GErr2GyLcu8K1/KumoUoKuUzjlVHJf/DLoM7vaHB8Nkc3mcByXnp44YpSDQ91jpFPo//gHIpfDS/bhvOlNk0rGZwPfU08xPNyZz0C7oiGSyQSu6zYcUdEJlPKIRiOEQiEGBobo6+spf4YK1XZdZQSi4vAQjYZZuzZwcGiGoCCtJ7S6PVdZJU023Kppkt7eBLmc/7NmsX51E9Fz/9dXqkWjIysNpRD5PDguhQ99iNwXvtT0c4RCVtXMc/xV/HgHh7mKYej09MTbZsFToRXtuXr42Vixlq6+W4MKHBw6RFCQ5jjtbM9ls9ma80QzbrEUi/S+bV/ka6tQNTzlRC4HUmPovj/jLdp0Wocf3Z4rFOyqamo8lS/YwcHhWRcGzIR2Cxxa2Z6rR8cEDtMgEDh0jm4vSIHsuw6zGQ3hz5Couvk2dc/9T3/0i1Gk9iyNCofBLmLddlvDx51uNEQulweafx3dQjabQwgxIdphprQzGmIyakVUzCbjHRyCiIoNl6AgTUK3RENkszksq7kZErl0SXWvqCbl8D1tyUsNHbOZ5NbRDg76DPeqZpNWOzh0IhpiMjri4NAUgYNDQOBlN4Zui4YYnQI67Z6/bvgiBqXqS6aFQBn1xQONtudqUSz680ixWLghgUO3ks8XsSyLWCwyI4HDZN5zAGJoCOO+P6E/9RQ4Dt4WW2C/4514m23WqpcAjERUxGLhrhE4jHdwmE5ERbE4hOcFAof1gaAglamlnovHIxPUc5UI8Wy2MEE9l8kUypvH0XK7Lj2j4dZsNkdvb4JwODQtgYOzxx7VmZ4JIXkVyjJm502TD52OV8/NJCwvm83R0xMnFLLmtMCh8josy5x2a62ees645x7Cl1+KXL0GUCAkuC7WL39B8aj3UPjghxpXQ3oe+sMPY/3fHeiPPQaeh7P99thHHkXpbW8HwyCbzZNIxDBNu2sEDhUHB7/o+w4Ovb3xCYar4bBFT08Mz/MdwHt6YgwOZoFA4DDX2eBFDZ1UzzVLUwIHpUgcdQT644+h4vGJszxKIdJp1Lx5DN7/twl5Q/XUc82yoQocplLPGQ/cT+Rr54Ft+/HplZagUoiBAWQmTeHU0yl8+CNTn1ypROSbF2D9+g6wbVTIAiEQhQJIDXu//ciefwHEYnNW4AAQjfr7s7lcgVgsEggcGqTbRQ0bbEGaLfVcsySTPTiOQzo9rsXiuhh/fQDr5l+hvfQiKhSi9Lb9KB53HGJomMTJJyDWrUNZ1shKqVTyZd+hMOlLf0jpwAOrh5tpe64eQgiSyQS2XapO5M9FpuPgMKV6znWJf+j9aM8+i7t4i0nbq3LNGhCQvvoavE0W1X++H15K6CdXoOJx/0JkNLkcct06ikceRe78C+acg8NoMYbrukgpWbMmcHCYDt1ekLppV7NjzKZ6rlkmEziIoSESp5xE/H2nYd51J9pTT6P/85+EL/o+ve/YH/0/T5C64Sacfd+K8DxkKoVIpRClEu5OO5O+8qpqMZqueq4ZNiSBQ6PqOf3fj6O9+CLuRgtq7vV58+YhhlMY991X97zE8DDWzb9CmebEYgQQiaDiccx770W+9NKcEjjEYpFqMXIcl3Xr/FVdIHBYv5i73wpNML49l07nJrTnujW5dYLAwXWJf/TD6A/+HWWFxroaKIXIZIh+8VzSl19B6hc3oD37DPrjj4Pr4m63Hc4b96h+AY5uz7V7+HN9FziMb88NDdV345avvgp2CerI85EShB8jXw/jL39GDA7WjY5X0Sjy1Vex7r6T/CfP6nqBQzwerbap/Q5Brq7AoVAIHBzmMhtEQeo29VyzjBY4OPfcg/7QQxOLUfkFq1gMkU4Rueh7DB9wAO7rtsd93fZj7jZpe8510V58AZFO4/X04m21VcuNTddXgUMt9VxdNM1XQk6lhlRqSlGDHBjw/2eS+4lsDjkwgEgNQ6lE+LJLkUuWUjz2OLJvf1vnBQ5TvF5N09G0kcKSTmdJJGIAEwQOiUSMwcFhwmGLeDxCKpUnEDjMTdb7gtSt6rlmcF2/xRKJhCjecjMoD2qF1wnhp60+8wz6vx/H2W33UT+aRD1XcjDvuhPrxhvQn3sOHAcMHWeHHSmedDL2gQe1rDA5jkuhYBOJhCkW7TnbZimVHIpFm2g0TDgcasp7ztlhR1Q8jhge9gMLJ38ikBrujjvVPZaKRPwves8bI2IRg4NoK1b4qkoh/PeGlJh/uBfzrw+Q++QnsT/+8ebGC6aB/sjDhK75Gea99yAKBbyFG1M46WQKp5yKmj+/er9wOFQdhs3l8kQifmvdtkskEtGqwCEWi5BO+5Jwy7JIpbLlPbEipZIKVklzkG5qHLeUbhlubTUVBwdz2VL/hnofOsNAOC7ylVeqN0063FpyCF9yMdGvfBn9yf/ghUN4fUlUKIT+2GNEv/B5Qldd2eLXMfcdHKQU5f/Ksons9IdbvS22oPSmNyHXrq3K8MegFNqrK/A22QR737fWPVbpLXuiIhFEJlO9TeQLfjHyPH9vqZziqvr78TZdhEIRufRSCvf+oa0ODuEffJ+eo4/C+vWvIZdDeQq5fDmR71xI7zv3Q3vySQzDIJlMEIn4Iw6Dg6my6XDg4LChsN4VpGaSWwFSqRFhQ7cnt2azOT+FdapvPs8DAcry5eq9vXGi0TDFYonBwVS1pWT85c+Efn4tyrJ82XE0CqaJikbxFm2C0nXCP7kC/eGHWvYa5rrAYXRya6FQLIsCmntPFD7yMdyttkJbsgQxuK460CwyabQlS1CxOPmzPz1Bmj8eb4stKL31bYjhlD9/Boh168B1/Rh4pRCOg4pEUDG//aX6+6GQx7j5V20TOJi33UrkuxeilELpOhgmGAbKslCGgRwYoPfUk+jBxXV914oR1V/g4LAhsV4VpFBII5mMzin1XDPYdglnv/396ltnLkkUC6hoFOvtb62rnrNuvQVKds2Wkerrg0Ie6/bGPe8aoVi0KZUcYrG5s0qaTD1XmSuKxeoIE+rgbbYZ2Qu/i33QwQjHRVvyEtqSl5CpNM4b3kD26+dT2mffho6V+99zcXbZBbl6NXL1ar/AgS/1L5VQloW72WZjVtYqFsN48O/kV76G53mt/ftQisglF/utRMOcsKIXUvh7Xq+9Rv6qq0mlxopAxjs45HK+eKji4GCaRlXg4AdciqrAQQg3KEpzjPViDmm0eq5CxVHAMPSuGG5tNfqa1fTs9zbI5fCisYmtO8dB5nNwxhmoiy+uOdwq0il6DjoAwE9srYEYHARdZ+hPf2lpTtFciaiYcri1RREV8pVX0J59BjyFt2gR7o47TnvvTgwNEfrl9Zh33I7+5JPlYmDgJZN48+dP/PvLZhGFIqlbb0ffasuWRlRo//kPvQcfgJIStJGV8ISXVCzi7LIrw7+7p8aRgoiKVtDtc0hzr1cyilrquUgkhGnqCCHwPIXjuIRC/gxPN6rnmsGZvxH2ty/EPOfTiHQKFQr5/nVKIYoFhOvCTjtR+N9zydVzFMgX/FVWLXFEBU0Dz0PYRVQLC9JcEDg0op4bLXCw7VLTr8PbbLMZe9ep3l7yH/sE+fedSc/BByKGh1ALF444QIxD2LbfQutJzMw/cRLkmtVlRZ3fjBHlfwmg8huqtMDla6tqvyYFmUyWnp44+XwxEDisp8zZll294VbT1MtFJYPjOFiWv6z3PK8s7Z477bl6pN99BN71v4Ddd0c4DjKTRuayyEgE733vI3XDTWRD4bpfjqqnByJRKNSf1BeFAiqRQEXq72M0Q7cKHKYbDdGuiIqmiUSwjzkWUUkKngylENks9n77o+J+blYrIypUIlF1lRejru0UI2p3AKEUKlFnhR5EVGwQzLkV0nSGWyORcLU9VylEoZB/dV8qOTiO29Hh1naQ3fetJB54gMLD/8B8dTlYFpmdd6EYjTV2AMuieNhhhH52NWqcXLiK54FtYx9x5OQ/nyEVgUM8HqVQsHEcp+XPMR2mO9xaoeLg4BcwG3cy1VyHKR55FNbttyFXrsTbeOOxf39KIV97DRWLUzz2uOrNox0cCgW7uYDIMs5uu6MWLkSsXAm6VnnasSgFUlA84ogpjuYLHJLJnjECh9HzSENDafr7e6sCh/7+JJGIST7vBqukOcCcWSHNVD1XUQ8Vi35br3J1JaVgeDjVFeq5ZvA8D08prLe8ieIhhzG45z5ji1Euh/b0U2j//S8iNbmJZvG9x6IWLECuXDlReuy6yFf9L7PiUe9p2+voFoHDaPWcL4tvrBhV8NVfzQscWo23eDGZ8y9A9c1DrngVsWYNYngYsXYtcvkKVDhC7otfwt111zGP88cLZiZwkFIST/YgP/Up/wPsOJMWI1GyUVaI4okn1T1eIHBY/5kTooZaw62RiDVhuDUcHjFGHT/cmsv5w62+iMG/CoxGIw2ZZHYb44dbdV0jlytU3ZDF4CCha6/BuuN2X2mlFCoWwz703RTOOGNCZLn+6D+IfuHzyJWrQJMow0CUSuB6eIsWkfn2dyZ8abWa2RQ41IuGaOZYrRA4tBK5bBnWnb/GvPNORDaDsizsgw7GPvKoCQ4eFfxB8OYEDiPDrYrscArzA+/HuutOAJTUqgpRgUIZBpkrrsQ+6OAGjx4IHJql20UNXV2QOhEN4X9wfF+1bmixNMJk0RCjIyrUa68R/8iH0J/6L8rw54kQApHLIQoF3M03I/2jK/C23mbMccXatZi//Q3m73+HHBrCmzfP/9I6+BBf+t0BOh1RMZV6rlmmG1HRMZSCYtF3fm+ghTXdiArDMIjFwkgpx7qIuy6h668jdNWVfkKxUqBpFA86mPwnPom7627TeAlTR1QUizZ9fT0MDaVRSpFMJoKICoKC1BSdjobwZ3Q8hoczNc6oO5gqGqISUeG9//1Yv/sdXn//RPWc6yLXrMbZaWdSv7qlLXtCM6GTERVTRkPMgOlEVHQzjUZUSCmJRsNYlln+nNUo7Eohly1D5HN4CxY2faFTK6IC/AHZgYEhotFIdcUURFT4dHtB6q5vI2YnGiKbzWEYBpbVOjlzK2k0GiKbzWEtfwXrgfv9SfzJpNyahteb9N2/H3ywQ6+gcTrh4DBd9VwzNBJRMRdoJKIiHA6RTCbQdZ1UKjNhuHUMQuBtsQXuDjvOcNUdODisj3RNQZpN77nRMyTdtpyf1HuuRoy4bZdw/nSfL9GO1VHZhUIIx8H4+1/bdNYzo10CBykF8XiUnp44SnlNec9Nh24TODRLLYHDRO+54Y65hQcCh/WTWS9I3eI9120zJPW85+phDw6ClAit/l+tAj81tkvJZv2r30qLdqbMVD3XLP7qW+/a1XejZLN5TNMfLveHyqP09MQm8Z7rHELIats6FguTTmfHiJri8Sj5fAHH8VdM2WwOz/OIxyNA+//uA6bPrDZTuykaoltmSCaNhqixIpoMp28eyvOQnocrxMg4/GiU8ifl+/tbdt6tplUODq1UzzVDqxwcZpuKg4O/wvAdUFplLzQTAgeH9YtZWSF1azTESItldmZhptOeq0Vp/3fg9fbC8HDNnr/IZFDhMMWGZbazw0wcHDrdnqtHt62+m8EwDDRNIoTAcZyOtufqETg4rF/MygrJNPVqe67bklvHp4B2gqnUc9NB9fRQPOEkwj++HJnJoMKRsYukfB6RzVI8/IgJsu9uo1kHh6aSW9tIt6y+m2G8es62S4RCFlLKGTk4THieFcv9cMh//hPheTiv3wV7773RXn0V4Ti422xDaa+9a6hCAweH9YVZk31L6S/7hYDe3ki5RVPEMHTCYf9KspHhVj/Aq7XJrZ2aIRnfnstmc9NeEU2K4xA5/+uE77gNbBuvbPsv7CJKSkpvezuZC78L9YQPXURPT7wcgFf/PTjb7bmp8FWSiuHh7nXOH82Y4dZRn7NkMoHrulUl64xQivBllxL53nf8lOJyFhSuOxJzLiUIgbvlluS+/BXsQw6d5DAe0WiEUCjEwMAQfX095ZZ/oTqPVLmoHRgYIhSyiEbDrF07hFLaBlOUul323XVzSOGwga5L8nkbw9CIRkMdj4boxAzJZMOtLcXzsB59hNjdd+E9+CCe4+LusAPFo99L6e37+Rk0c4SpHBzaNdzaarrRwWEyag63lpmJg8N4QlddSfSr/89fxZum796Qy401vNM0/x/XBSnJXHzpGO+9EQIHh6kICtIMkFIQDhtl5wVfPed/4Uwcbm01lbZPqx0cWtmea4TRDg6tbLF0mloODu0cbm0HXevgwDSGW5m+g8OkZLP0vXH3kfgUQGSzk0e5l3+O46BCIQb/9W9UMjnmLoGDw9R0e0Gaddl3PfxWgY3nQbHozw748wftj4ZotcCh0eHWVuPPkHRftMN0GS9w6MRwazvoVoHDtIZbaU1EhXX3XX4xquRrue7kxajyM+Gny4pCAetXN024SyBwmPt0dUEajecpCgUbXddbNpsyFa1ycGhKPacUxgP3E/vEx0i+cTeSu+5M4uijsG7+FeSmZ6mTzeawLH+GZK4y2sEhkYh1hXquGbrNwaHZ4dZGHBymQnvpRb8VV3l8vU5EZXVfzlYy/lZrqDtwcJjLzJmCBJRXSx6RSHi6qc5NMVMHh2aHW3Fdol/+EvH3n4H5m98gMhlEsYj++ONEP/85EiedgFizpuHzGJ0COpeRUqCU6vhwa6vpBgeHVgy3zjSiQhnG2JS+aSBKkxfNwMFhbjOnCpJSkM0Wy73uznyYm2mxzLQ9F77iR1g3/AKl66i+JCoeR8Vi/v/HYuhP/Jv4Jz8+ctXY4OvwjWe7q1XUCKPbc5WCPtf7/bPp4DDd9lw9Rjs4TBfnLXuOKOqgvtFvZTVZdgn3ensIXf1TQlf+BP1vfxtT1AIHh7lLV4saatHbG8YwdIaGUq2RSU/BdAQOM1bP5XIk3/5WxNCgHy8+GcUiolgkdd0v/A91g8w1gUMt9VynIyraRacFDlOp55qlaYGD59G7/9vRXngeZfkFQ2QyE1dMQozEZZRKvjxciJH4DKVwt9qa7PnfoPSuA8o3BQKHyQhEDW0gkymUr346s0pqRODQdHtuHOb9f0GsG/AzjGreyQTHwbr7rmkdey4JHOp5z83EwaGb6JTAod3ec00LHKQk84OLUJEIolj0FXTWJPvDFdd6255YjACEQFvyEolTTsIsfyYCgcPcZE4WJMcZLXDoTMujlsCh1eq56t5QvTmh8odRvPbatI/f7QKHRtRznYio6ASdEDi0sj1Xi5kIHJw3vJHhW27HeeMeCKUQrjvSnqsMxVbUdxWl3WT/AChF7FOfGCX6CQQOc405+2nOZn3LoRHzzfY+32QmmaPbc60adlSxqN+y8Lwpw/NUfPrL79ECh27wIqswvj03NFT/i7NYtAmF/PyrqRwcupl8vohlWcRikZY6OLSrPVeLXK6AZZnEYuFpOzi4u+7K8P/dhfaf/6D/+3FQHt7CjdH/+yTGQw+BbaMSPZi/uWvi6qhCWX0n0mms22+jePIpEwQOFYeGisChr6+nKnCIxyPk84WqwKFQ2LAcHLqFOVuQKgKHeDxMNBppe7oo+KuLZLKHWCyMpmltGW4t7fNWVDSKyOVqZxr5TW5K73xXU8+Rzebo7U0QDvtS39mmWe+5iu9gKGRN6uAwV2ilf+L44dZ2rIhqkc3mSSRimKbd1MWO+/rX477+9dU/lw44kIpPSvh738H8/W/rC3nKKyrjwQcpnnxK+SZf4OAXfV/g0NsbLzu9+O260Z53g4PDhMMW8XiEVCoPzL40f0NiTrbsKhQKDqWSU27dtP+No5Qfi2BZVtuGW9VGG2EffoTfL59M2up5iHQab/PF2E0WJNf1WyyRSPMzJK1gpsOtoyMq5vKVbKsCIjvRnqtHO8cLxGR7R5OhFDilCTdlMlnCYX/fqBJR4bfrRDWiwjSNakRFKGRhGDJo3XWYOV2QoHMCh8pwq6bJ8odctU3hlzv3Czh7vMmfPxoagmIRbBuRTiOGhvDmb0T60h/64oZmn2MWBQ6tjIYIBA6zm9w6nlY4OEyGu822vqCh3puk/DN3m7Eu9oHAYe4w5wtSuwUOk6nnMplsSxwcaqHiCVI/u5bcZ/8Hb9EihG0jCkVUOEzhlFNJ3XQz7s47z/h5ZkPg0Ork1g1Z4NAtya2jaYWDw2QUDz3MH4OYqiAJQfGkkyf5YSBwmAvMyTmk8QgBfX2+TNqfTWnFMetHQ3RshqRUQr68DFEq4S1ahIonWnr4lphkNkC7oyEajajodhqNqKgVDdEttDSiokzo6p8S/fzn/D+MU9dVilHhQx8h+/XzJ318sxEVAwMpPE/M6bZwhW6fQ1ovChJAKKQTj/t7ETMVODQy3NqJiIpOoGmS3t4EuVyhLQKHTkVD1I+oyKBpS5ByLQCeNw/P2xKluu/DOVVERafVc83SyoiK0YR/eCmR878+4vAghC8T9zwK7/8A2a9/Y0Q2Pim1Iyosy/eajMf9i1sh/CJUKjkMDmbWi4iKoCB1kJk6OEw3GqJdERWdpl0ODp2OhpjMwUHTnsQw/oYQaxkRlToo1U+ptDeuuzPtdo6fLpOtvqcTDdEttGv1LVe+ivWL6zH+8Q9wHJzXv57Cqac1lIA82sEhk8mV89X81qIQvlei53lomla1F4rHo+uNg0NQkDqIrgt6e6O4rjut1s1Mklv9FovH8HCm2dPuCpLJHhzHIZ2eeYtltpJbhRAkkwlsu1TeL3gKw/gtYOJ5CxnZMvWQchVC2Nj2Ibjuju0/uWkwfvXd7e25WkgpSSYTXbWS0zS/sJumvzfkui6aplEs2riuRyTit/Oi0Ug11K+3N46maQwMpJBy7u5TQvcXpDkvahhNMwKHpqIhRtGqiIrZphUCh1aq55phrMDBQ9cfATQ8bxPGvtUlnrcJSuno+sNAd80wjRY4dIt6rhnaJXBolkgkTG9vAk3TqhdeuZxfLE3TJJvNBwKHWWZul/tJaNTBoVXJrZM5OMxFZurg0Oxwa6sZcXB4jWLxNVx3Y4TwV8tKRRg96Oh5C9C0pWjay7jutrNyvpMhpUTX9eoextBQquvbc7WYysFBDAygP/kfcF3cLbfC23LLlp+DaRpEo765amWvVCkPTZOBg0OXsd4VpKkcHMa356a7IpqMioNDJBKa0wKHZhwcZqs9Vw/f+eA1THMFjrMcIQqAQKkorrtZecVklP9R1YLVDYxtz+WJRsPouo7rdn8abi0mc3AQr71G5PvfxbrzTsjnQAGGQWnvvcl/+hyc3Xaf8fP67Tm/9VYs2mSz+eoeaeDg0J3M/jq6DdRycJhpe64W3ZYC2izTcXCY7fZcPVx3NaXSH9G0pQjh4HlxPC+KEFl0/d/o+pNAJVepiBDrEGIVMHsXE5MNt1YiEmbq4DDbjHdwkCtfpeeEY7FuuhHsIioaRcVjKCkw/nwfiZNP8jOOZsBIe04yPJwmnc5OEOwEDg7dx3olahhNReBQLBa57rrree65Zzn//PPxPFrqPTeaDUXg0Gn13PRwMc1fYxiPYVmr8bw4tj26EVBCyjV43lYopdC0F3DdHVCqB6USuO72uO6OHZOET6WeW1/GC0YLHOQpp2De83vfq3H8BZxSiFQKtdFGDP75AYhMz4FlsvZcPZTy6OmJI6VkaCjNvHm91a5KLBZZ7wQOgahhlnAcxYMPPsRxxx3Hd7/7Hf7+97+zZs1Ay73nRrO+Cxxm6j3XCaR8FU1bSqm0PaXSfHR9GClHry4MwETXH8Ew/oFSfbjuDuU2nothPIBh3IMQjUuVhUgh5VKkXIIQAw0/rhHvufVl9V0VOKx8FfPP96FMc/J5ISFQsRhi9WrM3/224eNrmiSRiJFIxHAch8HBVINt58DBoZuYeyW+AdatG+Dyyy/hd7+7GyEExx13HGec8X5Cofb6na2vAofpRkPMJlKuBEpABNveFl0vYBirsO1YWdQAQqxByldxnN1xnLcAJn50tYbnxdD1x1DKolR6d93nEmIYXf8XmvYMQgwDCqViuO42OM4bUGrBpI+b7nBruyIqOk0uVyD05z8jbBuVqOM4omkIpTAfuB/76PdOedxIJEw4bOF5HsPDaUolp+FzCiIquov1riANDKzllFOOI51Osf32O3LuuV9gzz3f1BIHh0ZY3wQOiUQUwzBmXT3XGHa5MPhfSEolKBR2IxJ5BV1/GdddhRAFpEzheRvhODujVA9CrCrvNw3iF6YClnUHYFIqvQ0IIcQwQuRQSkOpPoTIYJq3ouv/Qak4nteHUknAQ9cfR9NWYNuHlldePjOJhmhlRMVsUhxOE5YSIevvwyiAfP3Pz3Tbc7UIBA7dw3pXkEzT4o1v3IM3vWlP3v3uI9E0rSpwKBSKbXPorlBpsfhtLXvOOjjI8heGYVQUSrmuECxMhhBDaNpzaNpTaNrzSPkcQth43ia47kaUSrui66+jWFyDEK+WW19RlOpDylfQtKcAygXFADyEeAnDuA8hBlBKwzCeKLfjJJ63CCFeQ9NeQakYQgwj5StACM/bGMfZBk1bgWHcT7F4LKCNUc81Y6ezvqy+SwsWEConw6pawhmlQEq8TTeb9Mf11HPNUhE49PTEyeeLVYHDunUp+vuTVYFDX19PVeDg74kVKZVUsEpqEeutqGE0zTo4zISOChw8D+2pp5DrBlCRCM7Or4dQc/b/49tzlYLeCgeHdiDlSgzjXqRciVK9+CuUh/FVdDFcd1s8bxui0QiO42Lbz2EYf8XztsZxdkHXHwVAqZ4xxxViJZ63sLzPJPC8fiAGOGjaM0j5Kp63CMd5E/51nUKIPEIMlW/fDk1bg+ueSDS6Y0u859YLgUOxSPJt+yLXrEYlEpMX1kIB4XoM//pO3J12GvOj0e25TCY3rfbcVGwIAoduFzXMjd/iDKk4OITDFqGQSaHQ/pZHR1osSmHe83us636O9txziJINmoa3cGOKR7+XwimnTiszabLh1opJZrHYXApoe8ljGH9CyjXlwVb/itt1t0PTnkMpgaY9i1IS2w4TCmko9QpClHDdzRFiACGyeN7GQB4ph8szSS4whJQvIcQgSm2KUosBiRBDgESpEFKuKw/VboU/5xRBKQMpX0XXF2KaEl3PYNteS8Ly1ovVt2WR+8QniH31K4hsFhWOjFgJKuXnfhVt7MMOG1OMWtWeq48vcEgme8YIHEa364aG0vT391YFDv39SSIRk3zeDVZJLWCDKEjQuINDq+hEi8W67lrCl13qR1Mk+1CWBY6DfG0V4UsvRnv2GbLnXwBGfTugesOtM3VwaCeathQpX8V1t2REMOrieT1ISbl9txYpnwAW4LoWhqHjOC6a9gSgo5RCiNVI+RpgI6UNDCJEGiHyKBVFylUo1V9u1a1DCAWE8D3xluO6mzPyUTLQtHBZct5HNpshn2/dKnl9EDgUTzkNuW6QyOWXIdMpf79IiHKEhMR+17vIfPs7QHvac7UIBA6zzwbRsqvQyoiKRmhni0X773+Jf/D94Hmo/v6Jd8hmkcND5D7/BYrHHV/z/BqJhmh3RMVUCLGmvHKReN58wI8HMIzfoGnP4nkVuxm73E5bDiikXImUryKEjetuAmyBac7HcZ4DluIXMRMooVQCf+9oqPKsZYGEKA/XhoBE+RwsNC2DUgZCZHGcN+J5GyGlhmnqCJHDcbLY9gJs+7247ti200yZKqJirhB66UVit92C86f7UKUS7vbbUzz+BEp77wNStrU9V5/aERWVdl1fXw9KKQYHh+nvT1IqOaRS+a6PqAhadl1EoeAQCq0fAgfrrv9DZDM1N36JRmF4GOu2WykecyyM20CejvfcaAeHYtFu2xXqeKRcjq4/iqa9WFa4ifLc0E6USnuULYFGWpKa9jya9jKe118uYArP2xrI43kboesv4Tir0bRNsG1RLl6rAVBqDUKUUMoqFycbIfL47TuvnAWXRQgXKTWUCiNEGH/vyME0DXRdw3FcHKcEDKPU9rjuFi3/vawvAofCVltjfu3raN+QDI2KqOhMe642gcBh9lhvB2NrkckUUEoRi01vArxZ/AE7j1istTNQxgMPoEIhv9XhOFAogG2PiXj2enqQS5ciX3555HFNDrfmcgU8T1XtX9qNlEswzdvR9SfKDgrb4nlb4Q+v/gnLurO8QvG/rITIjBI2GEg5yIgcV6HrLyHEcoR4BvgHhvFSuWjZKBVCiBLgIISNlGvK80xZ/Jkmr2wxVMIXJJfKLb0BpPStZaSUFIslbNsu2xAZOM7eVFZzrSabzSGEqAZJzlUymRxSSiKR0AyGW1uLEALbtrFtm0QiWh6G1QiHLbLZXFkt6V+kxeNRbLtEsWgTj0dRag7u63URG9QKCbpM4JDLYf7xD8iVK1HhEKV93oq31VaNHbRYhJKDXLYMOTwEngIBKhzB6+9HJZOgSfA8hF1syXBrNpvrkMChgGH8ASEyuO7rRt2uodRGuG4vmvYMSm2HXyDs8t5OAc/rwy8iBcAqOy4MI+ValNIRQsPzTHQ9jeMAuAiRxV8JwciKq7Lv4+Hvujv4knAX/2Mj8AuVwHVfwXEcQKJpRcDFtg/FcWZuEFqL9ULgwNiIioqn3HSHW9tDowIHMxA4tJBZLUjLl7/CDTdcx3//+yRLlrzI5psv5rrrfjXl45RSXH/9tdx++80MDQ2x7bbb8clPnsPOO7++oeeddYGD6xK66krCP/4RYnCdv8rxPDBM7Le9jdw3LsBbuHHdY6pwGH3lSr8IaZpffBSIbAYtl8XLZlGRCIRDmJttSiTZM+Ph1k4JHDTtJTRtVVm9NpoCUq4uh+sNYBgryoq65/GLxdgvASEy1X+U0vH3gAooVcJ1JUJY+Kugyv6eKP//6Nemyv+45Z9LQKFpDq4LpdJm5Uj0jcrPWcK296FUOpJ2NyDWB4FDxbgU/Nbw0FBr02WbJRA4zA6z2rJbsuRFHnzwb2y66aZssUXjOSjXX38tV199BccffxIXXvgD5s3r55xzPsGKFcsbenwlosKfnO9M6250iyVy4beJfPMbiKHBstNx3J/JEGD+8Q8kTjwesXp1zWOJVauQK5aD56IMA3QDpAaahjItlJTItWv8fw49lMimi1rmPZfN+i2WcLh9rSK/XQajr5eEWIdhPIKuP44Q6wCtLFpYXm6xLQHWIUSqqpCDHL5EeyRqwsdvz0lZKUQafrFR+GF9k12de+XHlIBSeXVpoJSFEAWEyOG621AsHl22HGo+6HA6+P6J+pzzTxzfnstkcui6NqOAyFZTcXDwW/y+g0M47BfPioNDJeY8kYiRzebwPI94PIL/fgmYLrNakPbZ523cdtvdnH/+hWy33fYNPaZYLHL99T/jhBNO4fjjT2aPPd7MeeddQCKR4IYbrm/4uWtFVLSLqknmM08T+umVoOuoeHzEYFIICIdRsRhyyRIil1xU81jWb3/j+4HF44iSA2rcm1/TwHWRnod78sktjYaYTkRF8ziMtWPJoOtPIEQaz1uIUvNQKolSCTxv6/IKZX7ZaaEEhHGc3VCqD78l5+8NQRalBEJ4QBbPAyFC+KsfB78gSUYK12gUUrp4XuWqV6CUiVIb47qvx3Vfj20fg+vuWn6uzrTQRq++58oV+WTREJU2cKf2KBsliKjoLLNakJr5QnvyySfIZrO84x3vqt5mGAZvf/v+PPTQ9DJUZkPgoK65FuE4qHCND56mga5j3nE7Ymho0rsYf/oDGCbulluhohFEqYSwi4iS7f/XtsE08WIxMtl8y41Q2y1w8IUJRSqFQcqVCJEqS74rX7p5wCqH7r0OCOE4b8J1t6RU2qu8f6MhxNryfe2yiKGIEBn8AiTxC0eleCgmFhKBpklA4Hk60AskGSmYbrkl6GKav8Oyrseyrsc0b0XTnqQT8ehdK3DwPMTq1ciVr4LtD1knkz2Ewxa5XIHBwdSYvaLRAoduIRA4dJY5J2pYtmwpAJtvvsWY2xcv3pLXXruBYrGAZTX2hp4NgYP6y5+RU5hLKstCZLNoTz2Fs/feE34uUimUroNp4m67LSKVQg4NIUolf+WVSOBGIsihYUSuPfNWrRU42Gjai0i5FCEq5+shxFqUSqJpr5adukdWAFIO47qblC1/BEqFAQvX3QRdfxYhlgMOSkXKBchvywnh4q+iBOCilFNeUAp8QYMv8/afw1/Zuq4qP16vnht4KBVDqSS6/mh5Hun15fNRSPkamrYUx9mJUumd+IO07aHrBA7FItbtt2H96ka0JUsBhZg3D3nyyRSPP4FsLDHp6MBogUOh0LnxgqkJBA6dYs4VpHQ6hWma1Y3QCvF4vLxpn264IEHnBQ7KcVH4V141l/TlN7BwJ1caefMXoL38sr9+kBLZNw/Z34+nFJ7r+e4D6TTKMPB6k215Ha0SOAixCsu6q7wHpPDfkiWkXI2mPU+ptDu+NLvy9+2VDU9DuO7WVIqUX3hKlErvAAxM81k8byFCFFBqGAjj7+sMI+W6srLOLyyuK8vPW2nf5cvtudFn6uH74w2Wz9MAQuXoiSKuOw/PW1S9tx/wV0TXn0SpBI7z1qZ/R43QNQKHbJbYOWdjPPCA/zcTiyE0CWvW4F14IfLmW+DyH8MWW0z68FyugGWNfLF3A4HAoXNscHNI4+m0wMHZcUdU+Zuu1ptU2DYYBm4NCXjpkENAKaTnous6Qgoc18N1XL/IKYUYHsbZdVe8Gh/8VjBTgYMQg1jWbUj5Eq67GNd9Ha67Na67fbkQ6ej6f6rWPr66bhVKRXGc3VGqf9SxSihl4hceF9fdjVLpIGz7nSi1BUpFUSqOUovL/7Xwi4pfjPz2sVNuz+l4XuWjUTVaoyJs8P/rIuXLSPkcnhdGymEmbmRbeN58NO1pphP41yzdIHCIXHwR5l/+Ar09iE02RiTieOEwbrIPb14/2ksvEvvsOVBnFZfN5jFNIxA4bIDMuYIUjyfKlh5je/PpdBohBPH49K0xOilwKB53vJ+KWSgghECMkypXDCbt/d+Bt2jTSY/hHXIIYpttkKtW4dk2juNUixxKIdauRYVCFE86ubraagczFTjo+hNo2jJcdxvAGvfTOLb91vLc0etRqhfH2RrHeTOl0r543sIx95YyVd5LcpByXXkfykKpRTjOlghhIMRg2VC1MuSq4XkLygO3UaQ0yt+T/ozRiMxbMNKy0xlR7GkI4SHlmvJK6bXy2SiEWIemPY2mvYBhPIph/AVfYt4+ZlvgIAYGsP7vDoiE0SIRlFK4rovyyp0AXcdLJtGeeRrjwb/XPM7o1Xc3EQgc2s+cK0iLF28BwMsvLxtz+7JlS1mwYOG02nWj6ZTAofTWt1Ha/x2QL0A+P7ZeOA4ilUYlesif9ekJj5VSEI9H6dl8U9xLLsFZvBhWrEC+uhIxOIhYs8aXg+s6+U+fQ+nt+7X1tcBMBA5FNO1xPK+X2gFnobJoYRGO83o8b2M8bzPGFy8pV+B5yXJhq/xCM2jaE+j6P9C0dXheoqzMi+K6G6NUb9l/biN0fX5ZpdcLbFLer4qW/6sz4nkXHvVPtGwxZFKRi+v60wixFk17AsP4e3ll9BpCrMIw/kgo9EukfHGav6fpMZsCh9ijjyBTKURPD67r4bneRMGiFUI4LsZ999U9ViBw2DCZcwVp5513IRqNct99f6je5jgO999/H3vuuU/Tx60IHHRdJxRqY8tD00hffCn2u9+Nct0RQcKwL0DwFm1C+qdX4+6445iHhcMWyWQPhqGTTmcZ3mQzUldfS+6zn8PddlswdFQySfGkk0lfdTXFE05s32sYRzabw7LMabVYhMiW1Wl1oqwBPwDPwXHeihCFsq/dEJArr0KeAzRKpf3LLTwDz4uXPfCWopRVLmRb4bqvK0dJbIYQMQxDAGtwnJUoVUKpIpq2rnyM+QgxEm0+IqqofGQ0fDeIikgijBDDGMbD6PoLeF4Uz9sEpfpQah6uuyVCDGOavy2bv7aH6nhByCoHEbafinrOLOR9naJiihWBQqSG6x5ztMChfeMFzeALHDRNGyNw8C3C/HZdOp1DCKr7YJomiUTMYJXUALMqaigUCjz44F8BWLVqJdlstlpodtvtjSSTSc4666OsWrWSm266AwDLsjjllDP42c9+Qm9vkq233obbb7+Z4eFhTjzxlBmdT8cEDrEYmcsuR3v6KeK/uRtt1UpsIbHf+jbsAw+CUYKNetEQqq+P4qmnUTz1tLJ1/+xsnDYncJCMyK7r4Vv1OM4b8Ly+cirskrJyzsRx9sB1dxwjKBAiX27hbQmMXvFaSLkIw/gnnpfFtrdDKbfsgWfgeVF8n7oUSrnlPalelJJImSufS2XfyTdV9WXffiH285dexHF2wF9FgZSZshpvPkqZSPkcmvYYnjd5O7YVdErgMD4awjZDvnOf44Be46ul/D5VfX1THj8QOGx4zGpBGhxcx5e//Pkxt1X+fMklPyaZ3APXdSfIWE855XRAceON1zM0NMg222zH979/KYtq7Lk0SkXgEI+HiUYjbY+ocHfYkdROO5FM9qAKRexRERXT9p6b5Td5NpujtzdBOBxqyBDTH2rdFE17HtftqXk/KVPY9psB8Lwt8LwtcJwUfmSEhZ/iOoK/T5TBcXYoZxyVUCqGlAaGoSHlalx3GM+bj+eFUCqBn3n0GuDgeRa67hce111ULjoKpVaUf8WVPSUHf49K4ifFDiOEP5grhFn+3s0BBTxvRyoeeUotLBetAZSaN63f8XRoeUCkUr6Br2mCpo2Jhqh4z4l99iWc7PPHEmoVnEIBZRjY7zqgwdeRJ5GIYZpG1+RxVQQOftH3BQ69vXHy+UJV4DBaEj44OEw4bBGPR0il8tRuUQdsUHlIjdLbG8YwdIaGUm2PqICRKIihoTSu646Jhshm83Mm8yYS8Q0yBwdTDc2QaNoTWNavyu4LE8UolSJRLJ4+ZgVUGxspn8U078HztkHKV9C05RhGDl2XuK7CcQYQYg1AecUyhO9vJ5HSj7EWooiUilJps3ILbxg//sKXevuUqgVFiHT5Z34chevuiF+wdFx3SzxvJM3WV+e9RLF4Mp63eQOvqXni8SiGoTM4mGq6XSRWr8b83W+w7r7b36c0DOT++yPeezS5HXeecPERvvgiwj/+EV48DpFx+7GlEnJgLaU3v4X0NT+fEIlSi0QihqZJBgfbr1RsFKUUpmnQ0+NnJcViflxGJaIiny9QLNr09fUwNJRGKUUymSgPA9dW2Labbs9DCgrSJOi6oLc3iuu6DA2193WLTBrtueeIRcKw9VYwf6NJ23NzhWSyB8dxSKcbUZS5mOZv0fW/o1SkbFBqAjk0bRUgse2DcZw96x5FiNVo2rPo+nMI8Sqa9iyuuyuatjmWlcTz1mHbOVw3jWE8iJSr8bOVDPy5Jt841c9R0hDiNXR9LUrNx3EMNO1lIF+Wlofxk2Z94QNkylZGPUi5AqUiuO6O+AmzG5Xti0Z/+dhI+Uq5ILWvbQczD4jUnn2G6Je/iFy6DEIWMhpDei5eKoUXCpN//wconnzK2NW5bRP90hcw774bPBfCYRASkc+DUjg77UT6hz9CLVgwjdchy3lDBXK5zsdR1EIpj56eOFJKhobSzJvXW+2qxGIRBgaGiEbDmKbJ2rWD9PbG0TSNgYEUUs5OcyooSHOUWMwkHLbIZLJtcXAQa9cSvvwyrFtuQWTSCECEw3iHH076Ix+jNA2z2W7CNA0SiRipVKbBFouDrv8TXf8nUq6i0gpz3cVlK6D6aatSvoBp/hEhhvC8JEIUMc1/omkaUiYplXaiUNgIsMtCh6fwo8fT5aK0MX4LpYgQ/tCrUkmkzGIYGrYtkfI5oFD1qPPVemGEcFBqHo6zU7lttw7X3QbX9WeoJj/flSgVplA4lYlS99YzfvXdKGJ4mPiHPoBcugSxxZZI0/ALiuuiXA+xdi3Cccie9zVfNToa18X8w71Yt9yM/vhjoMDddFOKxx6HfcQRqHh9IcsElCL29JNYy5aRLZaw37gH3mY1gik7iFIKTZMkkz2k09my4i5UTpRN4Hmq7ODQSz7vX2D29yfJZHKz5uAQFKQ5ihDQ1+eHqw0ODrd0pSJWraLn5BOQL74ImoaoBO0Vi1Aq4c2bR+ra63F33nnkMa+9hnXn/2Heew9ieBhvo42wD3s39iGHohLT/IC3meZaLHbZs85GqTCetwlTiUCFWItl3Ya/T7MZUkosy8AwHsd1V+A4oJRGqfQWhMihaf/Czyx6Bj87KY4fSaEBZtktooBSCYRwkDIO9OM4a4AsUmaRcgB/NTUPz9sEz9u4vMIq4rrbotSCcnHcYpIzLiDlUkqlA3GcN0/jdzMzensTKKWmJXAwb7+N6Le/hdxisT+k7bq+jHsUctkynDe+kcxll9fewyyV/GgVq7nia/z5PiLfOB/96aeoWmfoOvY730X2q1/DW7y4qeO2CqU8otEIoVCIgYEh+vp6sO0S+Xyh2q6TUhKPR6oCiGg0zNq1syNwCArSHCYU0onH/WTVVgoc4me+D/NPf4SK27dSeErhj8kqSKVwt9ySoXv/BLqO8dcHiH7+f5EDa1FCgq4jnJJ/5bn55mQuvgR3+x1adn4zRdMkvb2JtsdP6/pDGMZfcN3tynZSBkopisVVwGP4abIFXHdnKq0yEOUIi0y57eY7M/grntfKlkIhPG8BYGIY4DihsqfdNggxhJRry+02iT9cuxFC5LHtg/C8jTHN3yLEIEotQKkY/r7RAJDCcXahVDqITqyOKhiGTk9PvOEsLE2TJM7+FNrDD+MtXozjekx2RSbSaUQ+R+rqa/G2bP2K3rzrTuIf/yiUSihdR+i6HxJSLnLe/PkM33FnW91IGkORTPaWB/ZL9Pb6+0rhcAjTNMorJj+PbHBwmP7+JKWSQyqVR4jOChy6vSDNOS+7TlIoOIRCvoNDoVBsicBBf+lFzL8+gLAslKahPIUqTw8qfEmsiEbRli7F+PN9eJtuSvSzn0EOD+PN36i6EawAHAdt2VJin/wEqRtuQvX3137iDjLawaFYbNQkM4+mPVe2Chou78XsUN6PmWwF6KFpzyBlH+FwpDy0WCp/4SaQcmekfB5NS6Prj5eLzzBCeHhebzkLycOXhSukXIOfsyTwvE1RagF+MbPQtH7gCZR6DaU2xvOiOM5b8EP/NIQYQogSrrs1Si3Ato9E0x5H015AyrX4mUzzcJw9cZxd6WQxgkkCIutcg1bUc2pgAMcw8Oq855Vl+fNzU8wUNYNIpYid82lwHJRl+VJx8FdiponyPOTatcQ+/zlSN04d6tlOKg4OPT1x8vli1cGhInCoODj09fVUHRz8PbEipZIKZOCjCArSFGQyBXp7o8RikRkLHMJhi8g/HvbbcvH4iN3PKJRSCMOfazH/fB9KCOTQIN6ChRPbIrqON38jtOWvYP36Dgrv/8CMzq+V+DMkfntiKoGDEKuxrNvQtBcZEQysQtf/i+dtQrF4JJ639ZjHSOkRCgkMI47jeBPcof2WWi9K9SPEMK67MYbxGEp5ZdVbuKzis/ELUwE/K0mWY9A1wMZxkpjmQoQYRtNW4LpFPG9xWXJeWXU5OM7eVdWd5y3C8xbhOBUTV4nn9dPpQjSabNZ3q45EQpMKHEzTIBr1lWK5XAEtEkUv1o/O8D0XdVQsVvd+zWDdegsim/EDKEe97yvjdkJKlBAYf/sr2gvP426zbcvPoVHGOzhUBA4VB4dYLFKWhBeqkvCKg8PAQAohgq/hCt00At2VtMLBwTB0ensTRCJhnFQKVbnaq4FSCqEUWjqN9fvf+9lJta6iNA0lJdZttzZ1bu2kMQeHbLkYvVAWBGxb/kLfEtfdHinXYlm3IsSq6iPC4RDJZB9SRsnnh8v2RZOtwjSUiuG621Aq7YcfRxEFTDxvUzwviectqFoK+Yo4CyGK+DlGGkr1UCq56Prryj/3AAtNex5d/xeatgQpU+j6o1jWjWjav/GLHCjVh+dtVpasz14xgtoODuOTWwcHU+TzBUr77e8boNYRQoh1Azjbboe31dY179Msxl8f8KvPBGn4qE+OboDrov99ejlo7WHEwSEUsgIHhyYJClIDZLP+1XckEp7W/GnVe64njlIeQ0MpCsl5/uZsnQ+68jz/6i/ZC3bRb1nUwwohV68e2fTtEhoxydT1Z9C0F3Hd7ZgY+y1x3a2R8jV0/XEMwyCZTBCJhMjnbdLpxXheJQ5iMlQ5N2kHfO+5EH5Yn4tSPSg1v6yc8/BTZKMo1YMQfvtOqY3KBc3BdbNomsBx3kahcGx5haSXHcq3Kyvz1mCad2IYv6MTwXzTxf9y9Kp+jZMlt1YKu/2uA/A2WYT2yiuTvq/EunUgNeyj39ueoWzbnnTfCkZsiYQUICWiCwZmdV0nHo9WTVZ9g1vo7+9FKcozSzFKJYdIJIRlmWW3hwhCuEFRKhMUpAZoJqJigvfcsO+0YB94ECqe8OcyalEsojQdjj4aoWmIqeS6rosyzVl3a5iMqSIqNO2J6pf75AiUmk8k8jQ9PRqu6xf2XK5QXk1thJRLmWjxr5ByGa67Ea7rD6b6rbaNywVnbdmlIVG2ISqilInnhcrtvk3LLTw/9sJx8ki5GVLuiWE8gxBZXHdXlFqIvw8VLa+GFqPrT6Drj7bmF9hiKhEVfX21k1vBt6XK/b+v4C5YgFy6FLH6NV/EMDjo/7mQp3DyKdgHH9KW8/S23LIq+KmFcF1QCrdsuDwbCCGIRsP09sYRQjI8nCo7gdsUCv53hut6uK6LaRrVNIFEIla+wBX09SUIIip8goLUII1GVIxuzxUKRQYHh8com1QiQeHMM8FTUB4WHINtI2wb+4ADyb35LYjXvx6RrbMHoxSiWMR+xzu7siBNFVHhR0XULvKGoRMO96FpRdLp1aRSIxZKSvVSKh2Inyr7PFK+ihBrkfJVNO25crvtnfgGpxGU6sV1t8V131Bu1Rm47ubY9kE4zu543lZ43qaUSu/Atg8r++TtguO8AdvelVLpdYRCMTRtGZ63mMktYMIo1Yeu/5d2x01MF00buTAQQjA0lKqrgnR2fwOZS39I8fT3+UF7xQIoRentbyf7jW9S+OjH2vaeK5xwkn9sZ/KQSqUUyrZh0SJK++3XlnOYCssySSYTWJZFLpdnaCiFbTvlCAqzKnDQdcnAwHDZfcmujkOk09nq/FIQUeET7KZNg3oCh+l4z+U/dTZyzRqsm26EdNrvkwvht/GEoPS2t5P9zndQBZvQKaci//lPRCYz6eaxGBpChUMUjzmmLa+5FdQTOChlIWV2Ql3WNA3LMhBCUCqto1RysSdRLHveptj20Wjai+W4hwJ+dtKeZdVbb/l55uG6i9H158pJsxuPO5JZXq0lysOyFkotLJ+Xi6YtoVDYlljMxjA8XLd2LIJS85DyRaRcNUGMMVuM9p5LpTLE41FCIWtKBwdv8WLyn/gk+Q98EDE8DKEQqqe292CrcHfckeLhR2D9+g5f5q3rI8VPKV9QISV85StEeuIddXDQNI1YLIJh6BSLdjmMr9xGnIbAIRIJs3btIKZpBAKHMhv2q58mFYFDOGwRCplVB4fR3nMNzXpoGtlvfJPi4UcSuvEG9EceBs/D3WEHCiedTOkd76y6JWcOOpieU05B/Pw6yOf8KXdNg1IJkU6DYZD/5Nm4u+7W5lc/M7LZHIlEzHeFHtXzd92d0LS7gEWAQAiBaRoYho7juBSLRaRchevuVkP+DUr14DhvwHHegO/IPdnKReC6uyDlCqR8pSw0GFmxed58pPRtgfwZJaN8PimkHMR1N6dU2pdC4SkiERPblnXk7P7z+8ass8t49VxlRZTL5cureLsxB4dQCBXqbDZR5ns/ANfFuvuukQFb8AuTrpP94peRxx5HOByaoLJsB5WcqVDIwnVHTGUnuSeZjK9qHB1RMdpw1XdwGHEy7+9PEomYs+bg0C0Eg7HTpOLgYNs2V155JQ888AAXX3wxvb3JtnnPxaNhjBtvwL3qKuSSpf5KytBxdtyJwvvOoHTgQa1/0jYwmYODEKsJhX6Kn3W0NaZZGW4t4bouQqxFiBTF4im47vYzPgcpl2AYf0XKNeU5IokvQIjgONuhVC+a9lzZdFWVRQ07lLOUEuj6o8TjfwJ2rHNVXkTK5RSLJ7bdQLUW46Mhstn8hC/sZhwcOo5S6I/9C+uXv0B/5hmUrlPaZx+KJ51cTVROJhO4rtfWiArLMsviHEE+nyefry9a6VYHh24fjA0KUhP8+9+P8t3vfpslS5bQ39/P1Vdfw7x57RtKrZpkZnMU/vkYIpNG9fX5sxdz6GqqloODZT1NNHonQqQoleZh2yZC2GUTVEmpdACl0tthfNx702TRtGVl77wSSiVx3S1Qan75OfxMpEpBGnH4BiHWEA7fQDg8j2IxOukVspQv43kbUSyewGw0IUa35zKZXI2r+Ok7OHQr0/dPbJx67bmp6T4Hh6AgrUesXv0al176A+677w9IKTnllFM4/fQzCIWmG989fZo1yew2RkdUAESjYSzLxLafxbb/hhBPlfeBTDxvcVlYsAOtK0YzxzD+QCTyTzRtS7LZsUINIVYjRAbbfnc5hqJz1GrP1aMVERXdQKsjKsa357LZ2oW9Ft0YUREUpPWEYrHA0UcfxvDwMLvsshv/8z+f501v2q0jERUV/BaLx/Bwd6RnNksy2VN1SvY8RTabq17Z+kF3OZQymRjd0C0UMc0/E4s9i+sWsW0Lf5Ypg+fFcZx9cJw30qlzb6Q9V4uZRlR0C62MqJhue64e3RZR0e0FKRA1NIhhmBxxxNFsueVWHHDAwQghJhU4tJOWp4DOAoZhAApd1ygWi6TTY01r/YHV9qu4ZoaFbR9ALvd6YrGl2PbS8qzzm8qO3xt17EwmS26dDhUHh2kJHLoQz/PHC2YicJhZe64WgcBhOgQrpBnQzoiKWszVFos/VFxpz5UQwr+tm1JAm6GnJ44QdGyVXKGZ9lw95oTAoQGaETi0oj1Xj24SOHT7CikYjJ0BzTg4zJRsNlf9AM0VfO+5BLquk0plSKUyZDL1HRzmCtnsiH9ZJ6jlPTdTKg4OltWcX2O3kM3mMU1jCv/EESYbbm1lMQIQQpYVuIpYzJ/FC4f990s+XyQej5LPF3Ac3/POX5l5xOMRNjQHh6AgzZBGHRxaRS2TzG5krPdcgcHB4epe0VQODnMFx3EpFOyqDUw7qec9N1NGR1TM5TZRI/6J4LfnenrixOO+UnJoaHhGe0VTUYmoCIdDZWshP6Iinc5W/e98hwejGlERClkbnIPD3P0m6CIymUL56qczq6QRk8z2q/uawW8/ROnpiY3xnhuP79Ktpvzy6HZyuTzQvtdhmgbJZH3vuVYwF1ffk1FZfU/2OsZ6z4lRhb29X/rjHRx8t2+t6uAQiYSq+2DxeLSa7RWPR1Fqbu7rNUNQkFpAKyIqpovfYjG6rsUyWXuuloUSNBpR0d0opchm8+VVcut0Qu1qz9ViLq2+6zFa4DB69d2J9lx9RiIqRgscgoiKEYKC1CKajaholm5rsdRrz9Wj0RZLt1Ms2pRKTstWre1sz9VjfETFXKWSkRWLRTrenquFEALXdcuxE1FyOb+zEo9HSaWy1b2vTCZPJBJCCEE2m9+gIiqCgtQiNlSBQ6PtuXpMFVExV2iFwKFT7bl6rC8Ch1yugGkaHW/P1SMQONRnvZ1DWrZsKT/4wYU8+eQTRCJRDj74UD74wY+V52Bqc8wxh7Nq1coJt//xj3/DmiIor1BwCIV8gUOhUMRx2tv7ne0ZknA4VO59qxnZtowWOBSL7TfJbBejBQ7Foj2tK9qZDLe2mtGrb9suzckr88pwq1IKpRRDQ90zXlAROPT0xKsRFYlEtOrgUBE49PX1VAUO/tBvkVJJdUVHpF2slwUplUrxqU99hM0225xvfOM7rFmzmssu+wGFQoFzzvnfKR+/337v5IQTThlzm2k2drVYL6KiHeTzRSzLIhYLd8zBwTAMYrEwUsqWTMZD/YiKuUQul8eyDKLRcHUifypmOtzaDrJZf5gzEgnNKQeH8cOt+XyBnp44kUiooxEV9ZhOREU8HmXt2sGqwGF9j6hYL1/Zr399K7lclgsu+A6JhD/177ou3//+tznttDPp759f9/F9fX3svPPrm3ruWhEV7aRTDg7jh1unEixMl1oRFXOJisAhHo9SKNg4NQLmoPXDra1ktlff06VeNMRMHRzaQ+DgMBnr5R7SQw/9nT32eHO1GAG84x0H4HkejzzyUNuff30UOExXPdcMG4rAodPquWaZKwKHqdRzowUO3UIgcJic9bIgLVu2lM0332LMbfF4nHnz+lm2bOmUj7/nnt+x//57ccABb+Wzn/0UL774wrSef30SODSrnmuW9V3gMFvquWbpZoHDdNRz03Vw6ASBwGEi62XLLp1OEYtN9GyKx+OkUvU3N/fd923suOPOLFiwkFdfXcG1117Nxz72fq6++hcsKgeCNcJcFzi0uz1Xi/VV4GAYete25+rRaoGDfPFFQr/8BeZv7kZk0ngLF1I85jiKxxyLmjevoWM0ntw6wujVdze1gwOBw1jWyxXSTDj77P/hwAMPYdddd+eQQ97NZZf9BIAbbrh+2seaqw4OnWjP1WN9c3Do7U10fXuuHq1afVu33kLvwQcSvvInaK+uQGQy6M8+S/QbX6f3oHeh/ec/Ux9jBsOt9RwcZovAwWEs6+UKKR5PkM1OVJyl02kSicS0jtXf388uu+zGs88+Pe3zaFjg4LoY9/8F8/e/R2QyeBvNp3jke3B33XXazzkTgUM71HPNsj4IHMJhv/cvpb+BXSh0fhizFbRi9a0/+CDRz30WUSqhotFq0rHynwC5ejWJ953K0O//gOqfmL7cimiIVkRUtAf//fGZz3yajTfehC9+8Uvk8znS6Rzh8KINSuCwXhakxYu3mLBXlMlkGBhYy+LFW3T0XLJZG8syRs2mjP259vxzxD/4frQXX/Rv8BRIQfiqK7H3fSuZyy5H9fU1/HzNtFhmqz1Xj25tsTTCePWcaRrlC5K5WZBg9HhBpKmIivCPL59QjKpIiYpEkGvWYN18E4WPfrz6o2bac/XwxwtGvti7gYrAwfM8br31Fvbbbz8uvvgiPE9xzTU/Z926Nbzyygr23HMv4nFfEl4ROBQKrY+omE3Wy5bdnnvuzaOPPkI6PfLBqcSOv/nNe07rWGvXruGJJx5nhx2ai6OuJ3CQK5aTOP44tBdeROk6yrJQkbD/XykxH7ifxOmnQmH6zgeNtlhmuz1Xj7kmcKilnut0REW7mFLgYNuYv/0NsU9+nMSJxxP7+Ecx/+/XiGXLMB64H2UYE4tRhbLnXOimG6s3tct7rtsEDoahk0z2cu6556JpGt///vd4y1vewksvvcgtt/yKiy66iLPO+iRPPvkkzz77HD/84SW89trq9VLgsF4WpCOPfC+RSIRzz/0MjzzyEHff/X/88IcXc+SRR4+ZQTrrrI9y/PFHVf98772/47zzvsQ99/yWf/3rUe666w4+/vEPIqU2YVB2OtSKqAhf/kPkmtUoywRdH/mwCgGGgTIM9Mf+hXXH7dN6vkZMMjutnmuGuRRRUU8918mIinZSb7xAvvgiPe8+lNgnPoZ5913oDz/kF6ezP0XP8cciHAemMGxVmoZc9Vrbvee6ZbxASlG23YqjaZLNN9+KY489niVLltDT00t/fz9XXXUl7373u/E8j0svvYj//OcJfvnLX/Kzn/2UwcEU55zzae666471Rgbe3Z/yJkkkElx88Y/QNJ1zz/0MP/7xZRx++FF88pPnjLmf67pj+uEbb7yItWvXcMkl3+PTn/44P/7xZbzudTtwxRVXs8kmi2Z0ThMEDtks1i23+D30Wl+2mgYIQtf9fNrPV0vg0ArvuU7S7QKHRr3n2h1R0SkmW32LtWtJvP99aM8/h4pGUcnkyD/xOHLVSn+VX2dIGPDbCbFoR7znZlvg4HcmesptcofBwRzZbJH3ve9D9PXN42c/u5oTTzyVXC7H739/DwcddBD/+te/kFKyzTbbcOutt7BixSs89dRT/OAH32P58mWz8jpaTRBh3kFiMZNw2CKTyVJ6/N/0HvgulKb5q6NaFItgmqx7/qXa7Y4aGIZOT0+cdDpLsWiP8Z7LZnNdtyKqhWkaJBKxGfnltZpmvOcsyyQe961i6jk4dDvhsEUkEmZoKI3ruoQvv4zwd7+DSiQmXwW5LnLlSn+vKJmc/KBKIXI5+MAHyV1wQUfcuCOREOFwiMHBVMcEDhX5v65r5bj0IsXi2PfC7353N+ef/xXOPPNDPProIzzxxON85Svnc+GF3yAcjvD//t9XOPvsT7H77rtz+umnc/bZZ7PffvvxrW99H8ep/zqCCPOAKqMdHJhOuqyU0y5GMLbF0u3tuXp0S4ulQrPDra2OqJgtxjg4eB7WjTf4789aLTlNQ1kWuK5/gTUOAchcDgyD1LHHdiwaopMODuPbc7mczeBgdkIxAjjooEM577xvcvjhR/HpT38OTdN48skneN/7PsC6dQP8/e8P8s53Hshjjz3G2rVD7LnnXvz5z3/m/vv/0vbX0W6CFVKHCYV04vEwheEUxo47IgfWosK1v6BEoYC9776kb/jVtJ9rtHrOdd2uEixMF02T9PYmZnWotBXec7ru749ks/k5rbqrrr5fXUV019ejhIA672PyeWQqVW1PK8tCSOkXqVIJNI3MeV+neOppHXoFPp1YfVc6E/7MkUMmU5zW5/Dll5eRTPYRCoX44AdPJxaL8ZWvnM9JJx2DZVl873uX8OEPn8H8+Rtx/fW/wrJqtyGDFVLAGKoCh54EpVNP9a8sa811lEqgoHjq6dN+ntHquUKhWBYFzN0N9dkUOLTSe269Ezj0JkAIRAMrRBWNkf3CF/G23dYXORSL4HmU9tqb1NXXdLwYQXtX34ah09ubIBoNlyNa8gwP56d9Ubj55ouJx+MYhsEVV1zN9753KfPnb8THP/4pSiWbvr55nHjiqaxc+So33fTLlr+OThKskGYBXRf09kZxBwZQ73wX+nPPoqSEiizW86BUQngexUMPI/OjK6ZUKFWoNdza25tAKa9jERXtIpnswXGcjkVUjI6GyGRyLZEdCyFIJhPYdqnhiIpuREpBMtmDd/jhiPvvr70/BIjBQdxdd4M//hFD17D//QT5NWtx++fjbb555056EqSUZTue1gyDSymIRiNYlh89ns+XyOWKE2YQW4FSvn1QPp/nU5/6CHvvvS9nnPHBmvfv9hVSUJBmiarAYclSjLPPxvzDveB6IP2CpEIhCiefQu4LX4IpggFh4nBrNpvDdVy0J/6N+cD9aMUioS0Wk9n/nRT6GvMM60Y6JXBodzTEeiVwuOf3qDPOQJnm5G27QgFZLMKll+KeeBLZbGsKeytplcBhpu25VrB8+SvccMN1/Pe/T/LSSy9gmhagiESivOc9R3H22WdPmu+mlOLKK6/kl7/8JevWrSMWiwGQy+XYdNNNOfnkkznxxBPbeu5BQZolhIC+vigAg4PDiJeWYP7hXt86qL8f+9DDGjabnEw9J19+mejnP4fx73/7rT8pEEpBLEb+qPeQ+9znGyp03UgiEUPTJIODrU8B7WRya09PHCHoSJBjO+lNxBCf/CTihhtACt+NQdPAdRH5PMJ14YgjyV12GflS9/qvJZMJXNdrysGhEfVcp3jggT/zgx98h223fR0PPfQ3DMOoBpVeeun3icfjDA8PE41GOfLII6sF6ic/+QmXXHIJn/3sZ7n99tt56aWXEELw9a9/nWeeeYarr76a3XbbjWXLlpHL5Vi0aBEf/ehHOeKII1p27kFBmkWqAodCsanWTa32nFy1kvgpJ6MtW4YXj0EoVO7zK2QuC4UChcPeTfY736vfClSqKjuvOSs1C7RL4NCO9lw91iuBQyxC4dsXov/oR8h1A/7Fj24g5vfjnP4+Uh/+CJ7W3U5lzay+O9meaxTP85BSct11P+Oqq37Mpptuxi9+cQupVIrjjjuCTCbDd7/7XYrFIhdccAHxeJyhoSGKxSK77LILF110Efvvvz9f//rX+fGPf8zb3vY2Pvaxj/HOd76TcDjMN77xDWKxGM8//zyRSIRMJsM3v/lN9ttvP6644ooZnXt3v0PWc5qNqJjKey7006v8YjSvb0zBUVKg4gmEYWD97rfYR7+X0r5vnXj8pUuxfn27HxGQzaKsEPZBB2EfeRTu67af+QufIa2OqJit5NbxERVzddq+VHIo3XUX1h/uRQ0PjQgcenvIn/4+sh/8MHR5MYLp+yd2Q3tuMiqin4ce+jvz5vVX//zrX9+K6/pmrKVSiQMOOIBvfvObrFq1ig9/+MNcccUVPPvssxx33HEAXHjhhWy++eb85S9/IZvNEgqF2H777TnggAMA2GuvvVizZg0HH3wwoVCIf/zjH+y+++7E43He9KY3cc4557Bo0fQMBbrnsncDZboRFVN5z4l0CvPXv0aZxqSrH095EIkgXBfr5olScuOvD5A44zRCP70KsW4QFIh0mtD11xF//5mYv7m7+RfbQlrh4NANya3rg4OD9fNr0E49DR5+GKHrEI+jIhEYGMD69reJfeoTftt4DtCIg0Or1HPtZtmypdV9IPAL1N577838+fN56aWXuPHGG6sFauXKlYDvqr5mzRq22247EokEzz//PK+++ip33XUXuVyOk08+ecxzfOc732HnnXdGKcVGG23E5Zdfzuc//3mee+45jj32WNatWzetcw4K0ixTiajQdZ1QqHYqZ6Pec/KlJchM2v9CqIHneQjTxPjPE2Mfu2QJ0S9/ETE4iLfJItT8+aieHlR/P96iTRG5LJFvnI/2xBM1jtxZstkclmU2ZZLZLcmtSimy2XzZ57D7VxHj0Z5+iujXzkMoDxGPQ9kYGN1AxeJgmpi//U1T9lezweiIivHjBdMZbu0G0ukUuj7y2Vi2bClbbbUVPT09DA8Pc//991cL1Kuvvoqmaf53gxCcfPLJbLvttti2H2HjeR5HHXUU1113HTvttBP77LMPn/nMZ7j33nv5yle+wi677MLixYvZa6+9OPTQQ7nyyitZt24dd9xxx7TOOShIXcBoB4eJzvyt955TSuEphZRyzPNZv74dMTCAt3DjiXtGQuAtWIhIp7BuvWVGz98qmpkhadR7rpPMVQcHIQSxm3/lzxRFo1XP6THzVaYJniL082tqz9t1GblcgRUrVvCrX91IsewsUct7bi51WdPp1Jg8uJdeeqlaoAoF37Vi7733pr+/n2uuuYalS5dywgknVO9/yy23EIlE+OlPf8ppp53G3Xffzc4778xWW201YaZu4cKF9PX1sXr16mmdY1CQuoBaERXNREN4W22JisUQ+Xz9J7VteMMbfBsjAM/D/O1vUFaotoBB+Aoq874/QaY75pkajajohvZcPeZaREUlGkL/7W9QQvhfzKo8FwNjZ7AtC/nyy2gvPD87J9sEf/zjn/jBD77PDTdcPyfac5MRjydwnIldlOHhYXp6ekilRgqUafqijM033xxd11myZAmXXHIJO+20U/VxixYt4umnn2bPPfckEokQj8d5/PHHKUwSj7NkyRIGBgbYeuutp3XOQUHqEioODqC49tqfceSR7+app56ctveciicoHnEkomjXviLN5UDTKbz3mJGIimIRkc36V7S1cEqIUgkxNIT+5NRx052gEQeHbmnP1WOuODiMj4ZQmcxYn0Xlp8COeQ1SgqemvkjqIt71rgPYeOONufbaa1m+/JWub89NxuLFW5AZdeEYjycYGBhgzZo1bLXVVtXbh4eHWbhwIQCO42DbNkIItttuO1566aVqhM0uu+zC6tWrWbFiBZdccglnnnkmtm2zbNlYp3GlFOeffz4bbbQRhx122LTOOShIXcRvf3sPhx12GD/5yRVomoZpWk215wof+CDu4sXIgQHI56n2FTwPkUohszmKhxxCdo83j0RUWBZYock3n0sl5PLl6M88g1yxArlmNfFPfpzYxz6C/ti/ZviqZ4Droj3/HPbfH8R7+hmi4/bgurE9V49uFjgIIYhGwxOiIdyNN5lw4VNRC1aLUjkLyVuwoNOn3RThcIhNNlnIF77wBWzb5rzzvl4WH832mU2PPffcm4GBtdWLr8WLt+Dhhx9GSsk+++xDIjFSoPbc0w8ufe655yiV/KTp//73v9xzzz1EyvvRK1euZN68eVxxxRW87nWv43Wvex0A69atw3EcHMchlUpx8cUX89BDD3HhhRdWH9sowRxSF7BixXIuvvi7/P3vf0XXdc4880xOO+10hJiGI/g4JhuMxVOoaITi0e+tDsaOjqjQzvsq1i9/gbdo05Gr3lIJ7aWXEPkcSkqE6+Ilk6h58xBDQ6jeXrLfupDSPvu26LfRAEph/OEPmHfdifbiC4iSjbBC6DvuQO6QQynu/w6isWhHhltbTTc6OFiWWS6SvkXNaDfu0FVXEv36eb6IRo56vwq/ICnPg0yG0v7vIH1Ndwsbxg+3ZjIFPvGJj/HIIw/yrW99j333fftsn2JDFAoFHnzwr+TzeS688AKEgJNOOo1//ONhnnrqSY499lheeeUVHnvsMbbaaiuefvppFi1axLp16ygWi3ieRzQaRSmF67qYpkmpVKJQKHDWWWfx4IMP8sgjj9Q9h2984xscc8wx0z73oCB1AccffxQrViznTW96C5/+9P+w++5+33ZwcHhmV2VKoT3xBOYDf4F8HrXRAuyDDvJFC6OIx6MYhk7qwYeIvf9MRC7vX80KgXx5GXLdOpRh+BP3gLvlVqh4DJRCvroSb+FChm+9HUZJTNuGUoSu/RnWTTeCAm/+fN85uljEWDfgO0ifcQbemWeSyea7ekVUi25xcNA0jVgsgmHo5cKemxCWJ4aG6DnkIOSK5RCJjhk1EALIZFCGQeq6X+KUr8K7jXrDrS+/vIzTTjueBQsWctNNd8z2qTbEypWvcuyxk7snmKbJLrvsQjqdZtmyZRQKBebPn49pmixcuJCXX36ZNWvWANDT04MQgqGhIXp7exkaGuKUU05hm2224cknn+T222/nsMMO45hjjuGCCy6gUCjw8ssv84EPfIDPfOYzTZ17UJC6gHvv/R2hUIh99307QogZOzhMl4pJZqFQpHTTr4hc8A1EJo2yQmivrhhp+UmJt/EmePP7Rx7sOMjXXiN73texj3pP289Vf+RhIud9FRWJoPpHzkNKia5riDVrULkcqS9/BeeNe7T9fNrBbDs4VBJhQyGrbIOTo2SX0B95GOumG9Gffho0Sekte1E84QTwPOJnvA9t+XIUCjStOhwrIhHy3/s+2UPf3fHX0QiNDLfedtvNPP74vzjvvAu6en+vEVKp1Xz961/nscceIxKJkM/n2WmnnfjoRz/KTTfdxD333FMtUH/605+4+eab+dKXvsSiRYv47Gc/y+WXX87SpUvZaKONOP744/nQhz6EEIIjjzySZ599luOPP57zzjuv6fMLClKX0tsbxjB0hoZSDTs4zITRKaD8619Yt9yMdef/+fHTuo6X6EHNm+evjMYhX34F+6ijyJ5/QfvP84LzMf/0R9xttwP8L09d19CkxHU9FArtuefIv/MAcp/737afT7uoXLH7q+TObV5M2p4rFIidczbm736LcD2UoKxcAHSd/Mc/SeH978f89R1YN9+MtmolKhajeMih6GecgbHdtgwOprrKiaKbvOc6yXi37xdffLFmgXrttdf41re+RSgUwjAM/vSnPwFw+umn8+qrr3LvvfdWj3H44YcTCoW46qqrxoiL+vr62Hwabu5zbxJvAyGTKdDbGyUWi3SkdZPPF7Esi1gszPCuu5LbdVfcnXcm+rWv4i7atH7CrSZhEulny8nlMB5/DC/ZB4CuaWi6hlIKu1SqtpNkMon1+L/IFYtz1kA2l8tjWQbRaLgjq+R67bnoFz6PdfddeKaFCpsj+4vKV86FL7kI1dtL4YwzJ2R3SSlIlldc2ezsq+zGt+dyOXvWvedmk6233pprrrmm+udKgfr4xz9ONBrlmGOO4dOf/vQYd3DP83BHCVn+/e9/47ou2Wx2ghv4e97zHr71rW81fD7BCqmLicUswmGTTCZLoWC3/flGCxyKRRv94YeIf/QjeL09vkHrZCiFXL6cwvs/SP7sT7f1/EQqRfyM0xGWhTa/HyEEruPijFN5aUNDGAJSV1+DHeo+xVqjdELgMGl7btS+m/b8c/QcfKA/g1bjdykyadS8eQz+7aFJ3yejV9/uLA7Hdqv3XCfp9jykQPbdxWSzxZoODu2gmgIa9Z/PeeMeuFtu6cvHayDSaQhHsA8+uO3nJ+MxtHl9GIU8ylPYxdKEYgSg0mncRA/RjfonOcrcod0ODpXhVsuyyOXyDA1NlMVbt90KrusPTNdAhcPItWsx//iHSX+ezxfL4wXTkwC3irniPRcQFKSuppaDQzvJZnPlq+Yw6DqFD3wQTBOxerWfZDvq5EQmg0ilsA84oO0u4JFImN7+PsRhh+EOpygVi/4G+nhcF5HLUXjnO5GGMaWDQ7fTDgeH8cOtQ0PDY6Tco5HLl/t/7/WuiDQdJSXylZdr3iWbzWEYOpZVZ/C6xcw177mAYA+p62k2oqJZPE+Ry+WJRMIUCjb2IYciUinCl1yMfPVVf+peSig5YFnYhxxK9otfrv+FNQPGR0MU9tqX6K/vRHvxRdwtt/Rj3yuUSmhLXsLdemsK+70D0cKIitmilREV49tzw8PpKWXxyrL8eaK6d1K+qq7OKmr06tu2S20XOATtublJsIc0B9B1QW9vFNd1Ozab0tubQCmP4WHfekQuX475m7vR//EIwrZxt94G+93vxtlt97aE99VLbtVeeJ7wdy5Ee+kloPylaRdBKdyttyX3P5/DK3toJZM9OI5DOp1t+Tl2CiEEyWQC2y41LXCoN9xaD/P2W4mffRbeuBmjMRQLCM9j+Le/x93udTWPNXq8oF0Chw1VPdco3b6HFBSkabJs2VJ+8IMLefLJJ4hEohx88KF88IMfwzDqRyAopbj++mu5/fabGRoaYtttt+OTnzyHnXd+fUPPO9sCh07SUHJrJoPx4IMYf/8rcmgQL9lHae99KO2515gB3WZSQLuRZgUOjQy31iWfJ/m2fRCrV/txEuNXwp6HzGaw3/pW0tffMOXh2iVw6Mbk1m4kKEjrEalUilNPPY7NNtucU089gzVrVnPZZT/gwAMP4Zxz6s+8XHfdNVx99RV85COfYOutt+W2227m0Ucf4Wc/+wWLFm065XMLAX19UaAFDg4NUnFw6NTztSu5NZGIoWmSwcFUS443W0zHwWEq9dx0MP72V+IfOBORzeFZJhj+PpAoFsBx8DbZhNRNt+A1OG/ir74Vw8Ot+U4J2nONExSk9YjrrvsZP//51dx6610kEj0A/PrXt/H973+bW2+9i/7++ZM+rlgscsQRB3L00cfx4Q9/HIBSqcSJJx7Nnnvuw2c/+/mGnn82HRzaOUNSrz3XquP39iY6Gk/eDqoODmsG8O66C+O+PyGHh/Hmz8c+6GBKb30bGEbT7bm6z/3Yv4h89zvoDz3o5x8BKhTCPuhgcp/7PN6mU19UVWjV6jtoz02fbi9IgahhGjz00N/ZY483V4sRwDvecQDf/e43eeSRhzj00MMnfdyTTz5BNpvlHe94V/U2wzB4+9v35y9/ua/h559tgUM7ZkhGt+ca2WRvhtERFXNd4FB85FGin/oEaskSlOf53n2eh3nXnXg77gRXXYWx9VbNtefqPffubyD1ixvQXnge7YUXUFLD2XVXVBMO3jMVOATDresvgex7GixbtpTNN99izG3xeJx58/pZtmxp3ccBEx67ePGWvPbaKorFxq/afRt81bGZjpEZktbOwnQ6GiKXK+B53Rnt0CjylVfQ338GLFkC8+ahFi7E22gjvIULEb296E/+B+3MMxhe9ko586n139DuNttiH3wIpQMPbKoYVRgZL5ieLH99SG4NqE2wQpoG6XSKWGzikjcej5NK1d6fSKdTmKaJNc7GJh6Po5QinU5j1ZHMjsZxFIVCiXDYJBQyOyJwyGZz9PTEsSxzxgKHdrfn6pHN5kgkYhSL9pwUOIR+cT1y1Sq8BQvQdB3leiB8Y1k0DW/ePMRTTyFvvx1OOXW2T7cu0119T2zPFdrenlu+/BVuuOE6/vvfJ1my5EU233wx1133qykfN1MB04ZMsEKag8y2g0OzzHZyq22XsO3S3FwlZbOYd/4aZVnVyHCpSTQpq7k1StdBCKxbpv7S7AYacXCYzeHWJUte5MEH/8amm27KFlts2fDjrr/+Wq6++gqOP/4kLrzwB8yb188553yCFSuWt/Fs1w+CggQUiwVOOum9nHTSe8e0z1KpYY488iA+8pEzcV2XeDxBNpuZ8Ph0Ol3Npp+MeDyBbdsUi2M3l9PpNEII4vHpbTR2ysFBZNLo/3wU/eGHyC1ZOuLgME26Kbk1m80hpZxzDg5y7VpEJosKlaPayxcGnlJjiroKhdCWr/BTWucA9RwcZrs9t88+b+O22+7m/PMvZLvtGnMiKRaLXH/9zzjhhFM4/viT2WOPN3PeeReQSCS44Ybr23zGc5+gIAGWFeKLXzyPFSuW85OfXF69/Xvf+zaZTIYvfOEraJrG4sVbTNgrymQyDAysZfHiLWoev/Kzl18emz2/bNlSFixY2HC7bjSFgkOp5Asc9HpO3E0gBgaIfO08evfek8QxR5M4/jh69tkL5/TTCS15Ca3WgOQ4NE2SSMRIJGI4jsPgYGrWVW6jBQ6yDQO9bcM0EFKgCYEQAs/zUEpNzOfxPJQm2zKs3A5KJYdcLs+tt97Ma6+9BnSP91wz7496AqaHHvpbK09vvWRuvGs7wE477cxJJ53GzTffyL///Rj33fcH/vjHe/jwhz/B5psvBvyM+kcffYR0ekQCf999f0BKyZvfXDsNc+eddyEajXLffSPmk47jcP/997Hnnvs0fc7tEDiI1avpOe4YQj+9EpHOoKwQKhQCx0G/9VY46CBizz095XFmuz1Xj7kmcNA0jfh22yK32w7S/kCpGrUyGv3FKQoFSnvtPWcKEsCSJUv55je/yde+9hWi0fCc9p5rpYBpQ2TuvGs7wJlnfogtt9yK88//Kt/73rfZbbc3cOyxJ1R/fuSR7yUSiXDuuZ/hkUce4u67/48f/vBijjzy6DEzSGed9VGOP/6o6p8ty+KUU87gxhuv51e/uoF//vMffPWrX2B4eJgTTzyl6fOtCBx0XScUao1pZfRLX0B74XlUJIqKRkHXQddR4QheLAaDgxhnnoklJ99M6qb2XD2y2RyWZWKa9R02ZhMhBNFomN7eOELTyB1zrC+Rzo/MhHmehxQCgUCk0yjDxH7vMbN41tNnwYKFHHzwwTz++OP87ne/ndPquUYETAG1CQrSKAzD4Nxz/x8rV64gl8vxhS98ZUxLJJFIcPHFP0LTdM499zP8+MeXcfjhR/HJT54z5jiu605QDZ1yyumcccYHufHG6/mf/zmL1atX8/3vX9qQS0M9WilwkEuXYv7pj3iGOblvmZB4oTBq2TKif/7TmOfrxvZcPbpd4DBZNETuyPdgv/NdyOEUYt06KJVQnocqFpEDaxH5AsWTTqK071tn+/QbptKeO/fccwmHI1x44YUsX746cFrYQAlk3+N4+OEHAbDtIsuXv8Immywa8/MtttiSiy++fLKHVrnssp9MuE0IwamnnsGpp57RupNlROAQj4eJRiMzcnAw7v8zwrZBaoi1a6qxA8q0UJGI76yt67678733EjnkULLZfHPDrbaNed+f0B/7Fzgu7uLF2Icehpo3r+nzny7ZbI5eyyD2x3txn30WJSXuDjtS2mdf/3XOAnW950yTzHe/T/iKH2PdfJOfU+W6KN1AbL4Z9qmnkzvhxLY5r7eS8cOtiUQfZ5zxAS6//BJ++tMfc/bZ/zPbp9gUowVMo1dJzQqYNjSCgjSKF154nmuuuYpDDz2cF154jm996+v8/Oc3ERtl1tmNtMrBQXvxJSgUEKP7JEohCnlEsYCKxlDRKMpTuOk0oZCFaZrT9p4z/vZXIl/+Itry5aDwFWNKEfn+9yh88IPkP/Kx5vZAikWMv/wF4x+PgF3E23hj7EMOw1u8ePLXe/vteJddgrVyJUqV05WExNtiC7LnfhFn772nfw5N0nA0hGWR/9RZ5N//AYxHHvbbdPPmYb797ViJOGJwuO3RDjOllvfcsceeyG9+cye33XYzxxxzAptuutlsn+q0GS1g2nbb7aq3z0TAtCERFKQyjuNwwQVfpb9/Pmef/VleffVVPvjB07jkku/xhS98ZbZPb0oymQK9vVFisUhTERVicJDQTTdQbdqPvspWqhzIl0ZpEqFJ5KJFCCEQQjE4mGpYsKA/8jCxj38Mkc3gJXpG8ow8D5FOEb74Iig55M86e1rnr//rn0S/eC7y5ZcRrletc+GfXEHxyPeQ+/y5MOqK1bz9VqJf+xqUSjC/HwzDHzQtFpEvvUjsnLPI/OASnL32mtZ5NMNo77lcrkHvuWiU0v7vqP7RcT0sfKFGJ3wOm2Gq4VbDMPj85/8fl1zyvbmlgBzFaAFTpSC1QsC0oRAUpDLXXvtTnn/+OS6++EdEIlG22WZb3ve+D3DllT9i//3fyV577Tvbp1iXmTo4WLfegshMnLEC/OJULlQyk4FoFI4/nkwmN6a9NCVKEbnw24hMGm9e/9iiJyWqpxdSw4Su+gnF9x7TsGGn9t//EvvkxxHr1qGSfSjTF3goz0NkMoRu/CWiWCD7jW+CEIh0ishFPwDHQS1YgCd8ObUSwh88XbAQsWolke98m9Qtt7VNsTbjaIhRKKXIZvPE41EKBXtaERXtZjreczvv/Hp+8pNrOn6Ok1EoFHjwwb8CsGrVSrLZbFUpu9tubySZTHLWWR9l1aqV3HTTHcCIgOlnP/sJvb1Jtt56G26//eYZC5g2FIKCBDz77DP8/OdX8973Hscb3rBH9fZTTnkff/3rX/j2t7/Bddf9quv7v9lsEcvSR6WLNv5Y65ab/f8Jh8eouKpUipLr4r75zQxtviUUiuWr3jC2PfXzaU/8G/2///XVejX2OVQsjlw3gHXHbeQ/8amGzj38ox8i1w7gLVgwtnhIiUokQErMu++ieNzxOLvtjnnPPci1a/HKykilFJ5SSE3iOq6/b5bsQ3vxBfSHH275KqmZ5NZGKBZtQiGLWCzcsSDHqZjL0RCDg+v48pfHOvFX/nzJJT8mmdyjpoAJFDfeeD1DQ4Nss812LREwbQgEBQl43eu25y9/eXjC7ZqmceWVP5+FM2qOmQgc5GurUFJCKOTHUY92lRACUdljkZLs+z9Q/VE2myOZ7CESCU8ZUaE/+yyUbKjjalEpKNozzzR23i+/jPH3v6FisZorGRWNIldnMO+4A2e33f1jKzVGuOB5HpqmIaRAeQpCIRgaRH/2mZYWpKbac9Og4jtY2U+cLWbDe67VbLzxJvz1r4/WvU8nBUwbAkFBWs9oVuCg4nHkmjUo8BV1pukr7kq+CanSdZSuIwBv/ojL87RMMhttfSkaVoppL72IKBT8FmAthEBpGvrTT42+ccJzKs9DSomrXMoKh8bOt5HzbGF7rh6O41Io2KNWyZ0VOATREAEzYW7uHAbUpRkHB/vdh4/ZKxKG4e8V9faienpQsRjC83A33fT/t3fnAVHVax/Av2dWZpgBNMQ0FcglRUsxE0xTww0TlGvZ5lJmN8sFl0Svea++lppYal5cSvKmudzbve5d39erJmpqCLhkLq+vaYDgbsDs25nz/jEwOgwMA8xyBp7PX87hnDm/SeOZ3+88v+cB26WLw7XutqiwdIoBJBLA4CIbrzzV3NK5S/XnPEooBPBw3NXiOFvxUQBs+/a2Y5WCZ0WAEDCMbdlSKHh4bh05bG5lmEeqVnjvN7ROpwfg+0oU/q49RwIfBaQGqC4VHAyvvwkuKAiMwWD/R8Fx3MNv2GYzwDAwjJ9Q5aZZW5FMcZVFMiuwMTEwx3a3JUZU81uKUanABQfDlJLi1rgtT3UEFxwMRqut/iSOA8NaYeneHQBgGpII62NNbZtLH6XXgyu8AcGlSxBe+xVMaRlEJ09CUFjo1lgqq2pzqy+qVlQkONjqHHp/EYQvtedI4KOA1EDVtoJDUMenwGzYAEgk4PR6cAaDrWK02QxGpwNjscA4IgWGdyZUeb1bLSoYBro5c8E1bQLBg/u251QVgcliAVPyOxhw0KdOg/XxFm59Ti4iAqbBg8HodU4zHvtty8rAyWUwjUixXRMaakuYEAjA3L1bfu8SCH+7Dub3B+DKgy9EIsi+WofQV/4A0cmTbo0HsC3PhYYqoVQGw2y2oLS0zOPPimpiNJpgNls83ljxUf5sDUEaJoZzc5H53j1+ZO0Q9wUFiaBUymAwGKtNcJBIxAgOlts3t5qPH0fQunWQ/HAQDGvLOGPbtoV+/AQY3xxddUmhcgIBgyZNQmEwGF0mOIjOnUXwX/4M4dX/A8qbzIHjYA0Ph37yVBhHj6lVtQHBzWIox78NYUE+OKUSnDzYdr3ZDKasDAAHw6TJ0E+a8vAijoN0+78gW5MBprgYApXKFhwZxrY3KjwcnEwGzsKCKfkdXNPHULZrN6wuMqUqZ89ptTq/1fETXL8O+d+3Qnr0CKx6Ayxt28IweizMCQku/w7dFcjZc41Zs2b8zhSmgNTAhYXJIBaLUFqqckhwqKlzK3P/PgR374KTy22VDtwMEDKZFHK5Le3YZYKD1QrRqVMQnz39sHTQwEG251Z1ILhxA8GfLIQoNweMwQAwtsm/NSIC+gnvwjhmbNWfQaNByGujIP75HDi5HJxUCsjkYIQCe5sHsFYwJb9DPyUV+ukzqrz/o9lzer3ns+dqIyhzPYIXLgCsHMCV/50KBIDVCnNcHNQbN4MLC6vTeztnzxlpRhRAKCARvxKJGISFBYNlWZSWqmEymRAaGgK5PAhWqxUajee/xYeFhYDjrCgrq2ajrRcJ/+8KRLm5YEwmW+mgvv0AefXJHcz9+wjr39f2olIwFAht3Vg5KwempATWli1ReuSY4/18lD3nLsmuHVBOnmSb7YnFjkGYZcuDUjxUO3bVasNv5ew5vd5M2XMBiO8BidK+G7iKBAer1YzMzC+xZcsWzJs3DyNG/MFr1bgr9sJIpRL3Kjh4ENvhKbAdnnL7fEFpCRjWAq6KGmNWqxVCgQAsY/vlzvz+uy0LUCDw2ubWerFaIU9fapsZSRyDEQOAK1+qE5/KhvjEcZhf6OvW29LyHPEVCkgNHMdx2Lv331iz5gvcuXMHrVq1Qrt2T3m1NcSjCQ7uVHDwJ06hACcQVp0Qwdn++wkEAnCsBVxoKCAQeH1za12JsrMhLCwEREKn5Un7X4FAYHt+tm1bjQGpIWxuJYGFAlIDduNGIT7/fClOn86BVCpFamoqxowZC7O5btXAa6M2FRz8yfp4C1hiYyHO/sm2IbjyzzkOAgAMy8KclIzQUCVvlucqExYW2GZwrlK9GQawWiH87Xq1p/hrc2tBQT5WrlyGCxfOQy4PRmLiS/jjHydBLHbdRPGVV5Jx+/Ytp+M//HDCqVEe4TcKSA1YevoinDt3Bi+80A9Tp85ETEy7KhMcvKFWFRz8zDh2HMR5ubZWDpXrFVo5oKwEUCgQ9O47YMs3t/p9ea4KXJCbrQ04rsrgC/hveU6lUiE19X20bt0Gixd/hnv37mL16pUwGAyYOXNOjdf37z8Ar7/uWLxUIvFMF2XiOxSQGrApU2ZAr9chNvZZAPVvUVFber2tSZlCIfNLgoO7TIOHQP/+B5B9+aVtH1JQECAUgjFbwBiNQLAcWL4cbIcOKC1V+Xu41TL3et5WCcNiedjWozKOAwQCW/r3I/y9PLdnzw7odFosWfIZQkJCAdg6L69YkY5x495BeHkh3Oo0bdoUXbo87YuhEi+ijbENWMeOnezBCKhbBYf6cqeCg98xDPTTZkCdsQbm3r1tlR1MJjAiEZhXXob5u39CM3QYRCIRJBLXy0ceUd5/qjJBURHkS5cgdHgSQocNRfDsNAjPn394WfPmMCYPty/LVfm+ZjNYsRg/d4u1vSdPNrdmZ59Ejx497cEIABISBsFqtSInJ9unYyH+QzOkRqY+LSrqImASHBgG5kGDYBk8GHKtGkEmI9iwJtCKJbblOaPJnsxgMpk9f3+jEdJdOxH0zd8gunTRVs/v6WdgGP8OjMNHQJb5FeSLF9mCSnmwEZ09g6BvN8L4yihoVnwBSCTQLvwEonNnIczPtyVqCIUPA1T562Xt22PHJwuxY8cuREa25kX2XEFBPoYNG+5wTKlU4rHHwlFQkF/j9QcO7Mf33++GSCRC166x+OCDVLRt285LoyXeQjOkRqaiRYVAYNsY6wtara48Tdq3xT5ry157rlUr6Fq0RCkEDs+KtFodBAIBZDLPtqFm1CqEvvoyFDOmQfTLeXvwEJ09A8XUyQjr3xfyTz62BZTykkaPJi5It/8LijlpAAAuPBxle2y9n7iK5TuTCbBaYenWDaot29D6w1nQaDRYsyaDN7Xn1GoVFArnPTJKpRIqletl0j59+mLmzNn44ou1mDlzDoqKijBp0gQUFxd5a7jES2iG1AjVtUVFXfE9wcHdza0sa4Veb4BcHgSj0eR22/aaKFKnQpSba5vNVC7rY7HYSiwBtudDj2IY2/ksC+nft0E3ZSqsbduBCw+HZuUqMH9ZAFFeHhiTEWz0k+CefhrBwTKMFouwd+8e7N27F0OGJKFr1+4e+Rz+Mn16mv3PXbvG4rnn4jF69Mv4+9+3YNasP7m4kvANzZAaqbq0qKgPd1tU+FJdWkPodAZYrZ5r7SD89SokB/7zMLi4Ut16p0AACAQI2rrV8fSmTWEePBimpGRIejxbXnlcAovFiunTZ4NhGCxfns6LdudKZQi0WufEF7VajRBXDR2rEB4ejmee6YYrVy57anjERyggNVKNPcGhPq0htFodpFKJRxIcpDt32P5QXTB6NAhVNyNjGIBlq9xbVF1riA4dOmLEiJG4fv0a/v3vPfX8FPUXGRnl9KxIo9HgwYP7iIyM8suYiO/Rkh3PHT9+DJmZ63DjRgEiIh7H2LFvOz38rezWrZsYNcr5nJiYLli/fqP9dWNMcPBE7TmTyQyTyeyRBAfm3r3yP7hRvNbVfzCGsT0zKmd7Rihzubn1vfcm4bffrkPuotafr8THP49vv/0GarUayvK9YFlZhyAQCNCzZ3yt3uv+/Xs4f/4chgx5yRtDJV5EAYnHfv75HObNS0NS0ghMm/YhTp/OxdKln0Aul+PFFwfWeP3EiZMRG9vD/rryL56KBAelUobgYHm1LSo8yV8VHDxde06r1SEsLAQyWVC9yjBxFWnOFa0vKhMK7W3kqw1aHAdwgOX53gDc39waEhKKNWsy6zx2Txox4mVs3/4d5s79EOPGvYN79+5izZpVGDFipMMepGnTPsDt27fw3Xe7AQAHD+7HyZPH0atXb4SHN8PNm0XYvHkjBAKh00ZZwn8UkHhs06avERPTGWlpHwEAunfvgeLiImzY8JVbAalVq9Y1bhZsDAkO3qg956kEB1NiImTr1tiW46pathMIHraWryoglaeBc0oF2NdeQ1hYSEDWngsJCcGqVeuwcuVnmDv3Q8jlwUhOTsF7701yOI9lWYd/My1aPIH79+/hr39dbp9dde/+HN59dyJatnzC1x+D1BMFJJ4ymUw4cyYPkyalOhwfOHAwDh36D27duokWLVp65F4NtYKDt1tD6HQGSKVSBAfLoFa7aKHugqXHc7A80/VhunfllhAVtekq1tpY9uE5FRtoBQKw6zMR2qqlT2vPeVpUVDRWrVrr8pzVq9c7vO7S5WlkZHzlzWERH6KkBp4qLi6CxWJBmzZRDscjI6MBwK3NgsuXL0Xfvj2RlDQI6emLoFKVVXleQ0twqEv2XF3VO8GBYaDO3ABri5a27rkmky3osGz5n61gIyNRtmMnzOVLcvafcxysnToB338P8aujYDJZUFKig1YbeMGIEIBmSLylVts2A1beLKhU2lJgXW0WFIslSEl5BXFx8VAolLh06QK+/fZv+N//vYTMzG8hqqIadENJcPB1awhPJDhYW7dG6X/vh+zLdQjauhmM2jZL5cLCYBg9FvpJk8E1bQrV830guPYrxLm5EHIcJLHdIOr9PFgrB61KHzDLc4RUhwKSD1Wksdakvmvf4eHhDhsCY2OfRXR0W8yePR1Hj2ZhwIBBTtcEeoKDPzu3eiLBgYuIgG7+AujSZkNYdANgGLCtWgOVK3i37wBJt64Ps+eocytpQCgg+VBW1iGkpy+q8bytW7fbZ0KVNwtWzJxqu1mwV6/ekMlkuHLlcpUBCQjMBAc+dG71aAUHmQxs+w7V/Ig6t5KGjQKSDyUnpyA5OcWtc00mE0QiEQoK8hEX18t+vOLZkbc2CwZSggOfOrd6IsGhOv5uDUGIr1BSA09JJBJ0794DR4784HD88OGDiIqKrnWG3YkTP0Kv16NTpxiX5wVCgoNQKERoqBJKZTDMZgtKS8t40UbckxUcANvmVj60hiDEV2iGxGNvvfUuUlMn4vPPlyIhYSDOnj2Ngwf3Y+HCTx3O69cvDomJwzB37nwAQEbGSggEAnTu3AUKhRKXL1/E5s0b0bFjDF54oX+N9+VrggMfludc8WQFB1qeI40RBSQe69q1GxYvXobMzHXYt28Pmjd/HHPm/BkJCY6bYlmWdXhuER0djZ07t2Pv3p0wGAxo1iwCSUnDMWHCxCoz7CrjY4IDn5bnXKlvggMtz5HGjOE4977/3rvn/ecJhF/CwmQQi0UoLVV5PcEBAGQyKeRyGUpL1fYEB39mz9WVXB4EmSwIJSUqtxMcKtee01P2HPGCZs2ce07xCT1DItXyZ4sKX25u9bTatqiQyYLsrSF8tbm1oCAf06dPwsCBfTB8+BCsXbsKZnPNy4wcx2Hz5o0YOXIYEhJ6Y+LE8bhw4RfvDZQ0KhSQSLX8meBQ19YQfOFOgkN1rSG8/axIpVIhNfV9WCwWLF78Gd57bxL27t2FjIwVNV67Zcsm/O1vX+G1197EsmUr8dhj4Zg5cwp1ZyUeQc+QiEu+THAQCoX2NucMw6C0tAwsy/8ZUVVcJTi40xrCm/bs2QGdToslSz5DSHm1cZZlsWJFOsaNe8ehuvajjEYjtmz5Bq+/PgavvTYagK1D6xtvjKTurMQjaIZEXKpIcLD9EvXO0l3l5TmVyrYfKahylYIAo9XqUFRUhBMnfrQf88fyXGXZ2SfRo0dPezACgISEQbBarcjJya72ugsXzkOr1Tok1YjFYvTr9yKys094dcykcaCARGpkMFhgNtsqOIhENbTZrqWqOreaTGbodHoEBUkhrKmtN4+xrBWbNm3C7NlpyM7+yS/Lc1UpKMh3KtqrVCrx2GPhLov2VvysqoK/d+7chtFY975QhAAUkIibPJ3gUNPm1kcTHAJZcnIKxGIxVq5cDrPZxIvNrWq1yqloL2ALSq6K9qrVKkgkEkilUqfrOI6DWk2ZuKR+KCARt3gqwaE22XPebFHhCzJZELp1exrjx49HUVERMjLWUmsIQlyggETcptUaYbVaIZfLqu2m7UpVy3OusucereBQl/v5S+XsuTFjxiMiojk2b97Ii2w0pTLEqWgvAKjVapdFe5XKEJhMJhiNjpuS1Wo1GIaBUsnvPS6E/yggEbfVNcGhPrXntFpdeckg/i/dVVd7TiAQY+rUGTCZTFi3LsPfw0RkZJTTs6KK1iiuivZW/KywsMDheEFBPpo3fxxSaWAnoRD/o4BEaqU2CQ6e2Nxa0aKC7wkONWXP9e8/AEOHJiE4ONi/AwUQH/888vJyHJ75ZGUdgkAgQM+e8dVe16XLMwgODkZW1iH7MYvFgmPHshAf39urYyaNA5UOIrUmEjEICwsGy7LVtqh4tPacXl//2nNhYSHgOGutW1R4m3PtOSPva8+pVCqMHfsqWrdug3Hj3sG9e3eRkbESgwcnYubMOfbzpk37ALdv38J33+22H9u8eSO++WY93n9/Ktq2bYddu/6FnJxT+OabrXjiiVZ++DSkNqh0EGlwXCU4eKs1BN8SHAK5NURISAhWrVoHoVCEuXM/xJdfrkZycgqmTp3pcB7Lsk5NE8eMeQvjx/8R//jHFqSlTcPdu3exYkUG7ty5jT59euDo0Syn+x04sB99+vTAhQvnvfq5SOCjGRKpE4YBmja1LT+VlJTh/v0HiIgIR1hYaPlMQefxcj9KZTDEYhFKSsr8mqlGrSGccRyHl19OQkxMZyxatMzhZ2lp01BYWOAw0yL+QTMk0iBVJDhoNBqsXv1XpKQk44svvvBq7Tl/Jzj4q/ZcIGAYBkOGvISTJ49Do3m4rFpSUoKcnGwMHjzUj6MjgYICEqkTq9WKXbt2Y8iQRGzbtg1RUVEYOvQlr/Yp8leCQyAvz/lSYuIwmEw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"}}]}}, 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"}}]}}}, "version_major": 2, "version_minor": 0} diff --git a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html index ee1424a85d..1f0a925895 100644 --- a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html +++ b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html @@ -2054,7 +2054,7 @@

    13.4. Exercise - build your model

    diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html index 5112f1f320..ac5585357a 100644 --- a/ml-fundamentals/classification/more-classifiers.html +++ b/ml-fundamentals/classification/more-classifiers.html @@ -2220,7 +2220,7 @@

    14.2.4. Exercise - apply logistic regres

    -
    Accuracy is 0.8065054211843202
    +
    Accuracy is 0.8023352793994996
     
    @@ -2243,10 +2243,10 @@

    14.2.4. Exercise - apply logistic regres

    -
    ingredients: Index(['barley', 'bean', 'buckwheat', 'carrot', 'katsuobushi', 'scallion',
    -       'seaweed', 'soy_sauce', 'soybean'],
    +
    ingredients: Index(['bell_pepper', 'buckwheat', 'cane_molasses', 'carrot', 'cilantro',
    +       'lime', 'peanut', 'sesame_oil', 'soy_sauce', 'vinegar'],
           dtype='object')
    -cuisine: japanese
    +cuisine: thai
     
    @@ -2296,24 +2296,24 @@

    14.2.4. Exercise - apply logistic regres - japanese - 0.991627 - - - korean - 0.004940 + thai + 0.442010 chinese - 0.002909 + 0.312328 - thai - 0.000457 + japanese + 0.240609 indian - 0.000067 + 0.004452 + + + korean + 0.000601 @@ -2334,15 +2334,15 @@

    14.2.4. Exercise - apply logistic regres
                  precision    recall  f1-score   support
     
    -     chinese       0.76      0.75      0.76       236
    -      indian       0.88      0.87      0.87       233
    -    japanese       0.74      0.74      0.74       227
    -      korean       0.84      0.83      0.83       259
    -        thai       0.80      0.84      0.82       244
    +     chinese       0.71      0.69      0.70       238
    +      indian       0.92      0.91      0.91       216
    +    japanese       0.79      0.78      0.79       261
    +      korean       0.83      0.79      0.81       247
    +        thai       0.77      0.86      0.81       237
     
    -    accuracy                           0.81      1199
    -   macro avg       0.81      0.81      0.81      1199
    -weighted avg       0.81      0.81      0.81      1199
    +    accuracy                           0.80      1199
    +   macro avg       0.81      0.80      0.80      1199
    +weighted avg       0.80      0.80      0.80      1199
     
    diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html index 5d4f35d018..9481700872 100644 --- a/ml-fundamentals/classification/yet-other-classifiers.html +++ b/ml-fundamentals/classification/yet-other-classifiers.html @@ -1875,18 +1875,18 @@

    14.3.4.1. Exercise - apply a linear SVC<

    -
    Accuracy (train) for Linear SVC: 79.9% 
    +
    Accuracy (train) for Linear SVC: 80.1% 
                   precision    recall  f1-score   support
     
    -     chinese       0.69      0.75      0.72       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.77      0.77      0.77       222
    -      korean       0.84      0.74      0.78       244
    -        thai       0.79      0.82      0.81       251
    +     chinese       0.66      0.74      0.70       229
    +      indian       0.88      0.92      0.90       232
    +    japanese       0.83      0.78      0.81       253
    +      korean       0.87      0.76      0.81       236
    +        thai       0.78      0.80      0.79       249
     
         accuracy                           0.80      1199
        macro avg       0.80      0.80      0.80      1199
    -weighted avg       0.80      0.80      0.80      1199
    +weighted avg       0.81      0.80      0.80      1199
     
    @@ -1910,31 +1910,33 @@

    14.3.5.1. Exercise - apply the K-Neighbo

    -
    Accuracy (train) for Linear SVC: 79.9% 
    +
    Accuracy (train) for Linear SVC: 80.1% 
                   precision    recall  f1-score   support
     
    -     chinese       0.69      0.75      0.72       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.77      0.77      0.77       222
    -      korean       0.84      0.74      0.78       244
    -        thai       0.79      0.82      0.81       251
    +     chinese       0.66      0.74      0.70       229
    +      indian       0.88      0.92      0.90       232
    +    japanese       0.83      0.78      0.81       253
    +      korean       0.87      0.76      0.81       236
    +        thai       0.78      0.80      0.79       249
     
         accuracy                           0.80      1199
        macro avg       0.80      0.80      0.80      1199
    -weighted avg       0.80      0.80      0.80      1199
    +weighted avg       0.81      0.80      0.80      1199
     
    -Accuracy (train) for KNN classifier: 72.6% 
    -              precision    recall  f1-score   support
    +Accuracy (train) for KNN classifier: 74.6% 
    +
    +
    +
                  precision    recall  f1-score   support
     
    -     chinese       0.63      0.71      0.67       230
    -      indian       0.87      0.79      0.83       252
    -    japanese       0.63      0.86      0.73       222
    -      korean       0.92      0.54      0.68       244
    -        thai       0.70      0.73      0.72       251
    +     chinese       0.67      0.73      0.70       229
    +      indian       0.85      0.81      0.83       232
    +    japanese       0.66      0.85      0.74       253
    +      korean       0.89      0.58      0.71       236
    +        thai       0.74      0.75      0.74       249
     
    -    accuracy                           0.73      1199
    -   macro avg       0.75      0.73      0.72      1199
    -weighted avg       0.75      0.73      0.73      1199
    +    accuracy                           0.75      1199
    +   macro avg       0.76      0.74      0.75      1199
    +weighted avg       0.76      0.75      0.75      1199
     
    @@ -1962,41 +1964,41 @@

    14.3.6.1. Exercise - apply a Support Vec

    -
    Accuracy (train) for Linear SVC: 79.9% 
    +
    Accuracy (train) for Linear SVC: 80.1% 
                   precision    recall  f1-score   support
     
    -     chinese       0.69      0.75      0.72       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.77      0.77      0.77       222
    -      korean       0.84      0.74      0.78       244
    -        thai       0.79      0.82      0.81       251
    +     chinese       0.66      0.74      0.70       229
    +      indian       0.88      0.92      0.90       232
    +    japanese       0.83      0.78      0.81       253
    +      korean       0.87      0.76      0.81       236
    +        thai       0.78      0.80      0.79       249
     
         accuracy                           0.80      1199
        macro avg       0.80      0.80      0.80      1199
    -weighted avg       0.80      0.80      0.80      1199
    +weighted avg       0.81      0.80      0.80      1199
     
    -Accuracy (train) for KNN classifier: 72.6% 
    +Accuracy (train) for KNN classifier: 74.6% 
                   precision    recall  f1-score   support
     
    -     chinese       0.63      0.71      0.67       230
    -      indian       0.87      0.79      0.83       252
    -    japanese       0.63      0.86      0.73       222
    -      korean       0.92      0.54      0.68       244
    -        thai       0.70      0.73      0.72       251
    +     chinese       0.67      0.73      0.70       229
    +      indian       0.85      0.81      0.83       232
    +    japanese       0.66      0.85      0.74       253
    +      korean       0.89      0.58      0.71       236
    +        thai       0.74      0.75      0.74       249
     
    -    accuracy                           0.73      1199
    -   macro avg       0.75      0.73      0.72      1199
    -weighted avg       0.75      0.73      0.73      1199
    +    accuracy                           0.75      1199
    +   macro avg       0.76      0.74      0.75      1199
    +weighted avg       0.76      0.75      0.75      1199
     
    -
    Accuracy (train) for SVC: 83.9% 
    +
    Accuracy (train) for SVC: 83.8% 
                   precision    recall  f1-score   support
     
    -     chinese       0.76      0.79      0.78       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.83      0.82      0.83       222
    -      korean       0.91      0.80      0.85       244
    -        thai       0.79      0.87      0.83       251
    +     chinese       0.74      0.76      0.75       229
    +      indian       0.91      0.93      0.92       232
    +    japanese       0.86      0.82      0.84       253
    +      korean       0.90      0.81      0.85       236
    +        thai       0.79      0.87      0.83       249
     
         accuracy                           0.84      1199
        macro avg       0.84      0.84      0.84      1199
    @@ -2025,73 +2027,73 @@ 

    14.3.7. Ensemble Classifiers -
    Accuracy (train) for Linear SVC: 79.9% 
    +
    Accuracy (train) for Linear SVC: 80.1% 
                   precision    recall  f1-score   support
     
    -     chinese       0.69      0.75      0.72       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.77      0.77      0.77       222
    -      korean       0.84      0.74      0.78       244
    -        thai       0.79      0.82      0.81       251
    +     chinese       0.66      0.74      0.70       229
    +      indian       0.88      0.92      0.90       232
    +    japanese       0.83      0.78      0.81       253
    +      korean       0.87      0.76      0.81       236
    +        thai       0.78      0.80      0.79       249
     
         accuracy                           0.80      1199
        macro avg       0.80      0.80      0.80      1199
    -weighted avg       0.80      0.80      0.80      1199
    +weighted avg       0.81      0.80      0.80      1199
     
    -Accuracy (train) for KNN classifier: 72.6% 
    +Accuracy (train) for KNN classifier: 74.6% 
                   precision    recall  f1-score   support
     
    -     chinese       0.63      0.71      0.67       230
    -      indian       0.87      0.79      0.83       252
    -    japanese       0.63      0.86      0.73       222
    -      korean       0.92      0.54      0.68       244
    -        thai       0.70      0.73      0.72       251
    +     chinese       0.67      0.73      0.70       229
    +      indian       0.85      0.81      0.83       232
    +    japanese       0.66      0.85      0.74       253
    +      korean       0.89      0.58      0.71       236
    +        thai       0.74      0.75      0.74       249
     
    -    accuracy                           0.73      1199
    -   macro avg       0.75      0.73      0.72      1199
    -weighted avg       0.75      0.73      0.73      1199
    +    accuracy                           0.75      1199
    +   macro avg       0.76      0.74      0.75      1199
    +weighted avg       0.76      0.75      0.75      1199
     
    -
    Accuracy (train) for SVC: 83.9% 
    +
    Accuracy (train) for SVC: 83.8% 
                   precision    recall  f1-score   support
     
    -     chinese       0.76      0.79      0.78       230
    -      indian       0.91      0.91      0.91       252
    -    japanese       0.83      0.82      0.83       222
    -      korean       0.91      0.80      0.85       244
    -        thai       0.79      0.87      0.83       251
    +     chinese       0.74      0.76      0.75       229
    +      indian       0.91      0.93      0.92       232
    +    japanese       0.86      0.82      0.84       253
    +      korean       0.90      0.81      0.85       236
    +        thai       0.79      0.87      0.83       249
     
         accuracy                           0.84      1199
        macro avg       0.84      0.84      0.84      1199
     weighted avg       0.84      0.84      0.84      1199
     
    -
    Accuracy (train) for RFST: 85.2% 
    +
    Accuracy (train) for RFST: 85.9% 
                   precision    recall  f1-score   support
     
    -     chinese       0.80      0.82      0.81       230
    -      indian       0.93      0.90      0.92       252
    -    japanese       0.85      0.83      0.84       222
    -      korean       0.87      0.81      0.84       244
    -        thai       0.81      0.89      0.85       251
    +     chinese       0.82      0.80      0.81       229
    +      indian       0.92      0.94      0.93       232
    +    japanese       0.85      0.86      0.85       253
    +      korean       0.89      0.82      0.86       236
    +        thai       0.82      0.87      0.84       249
     
    -    accuracy                           0.85      1199
    -   macro avg       0.85      0.85      0.85      1199
    -weighted avg       0.85      0.85      0.85      1199
    +    accuracy                           0.86      1199
    +   macro avg       0.86      0.86      0.86      1199
    +weighted avg       0.86      0.86      0.86      1199
     
    -
    Accuracy (train) for ADA: 71.5% 
    +
    Accuracy (train) for ADA: 70.2% 
                   precision    recall  f1-score   support
     
    -     chinese       0.67      0.49      0.56       230
    -      indian       0.89      0.81      0.85       252
    -    japanese       0.64      0.68      0.66       222
    -      korean       0.66      0.79      0.72       244
    -        thai       0.72      0.78      0.75       251
    +     chinese       0.60      0.54      0.57       229
    +      indian       0.87      0.87      0.87       232
    +    japanese       0.68      0.57      0.62       253
    +      korean       0.66      0.76      0.70       236
    +        thai       0.70      0.78      0.74       249
     
    -    accuracy                           0.71      1199
    -   macro avg       0.72      0.71      0.71      1199
    -weighted avg       0.72      0.71      0.71      1199
    +    accuracy                           0.70      1199
    +   macro avg       0.70      0.70      0.70      1199
    +weighted avg       0.70      0.70      0.70      1199
     
    diff --git a/ml-fundamentals/parameter-optimization/gradient-descent.html b/ml-fundamentals/parameter-optimization/gradient-descent.html index 749b1c3705..16d24a04e2 100644 --- a/ml-fundamentals/parameter-optimization/gradient-descent.html +++ b/ml-fundamentals/parameter-optimization/gradient-descent.html @@ -2065,19 +2065,19 @@

    15.2.5.1. Linear regression With gradien

    -
    {'lr': 0.0003, 'n_iters': 5000, 'weights': array([ 0.45351552,  0.21147914,  0.01485353,  0.08785205,  0.14878446,
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    -
    -
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    <seaborn.axisgrid.PairGrid at 0x7f93dee0aac0>
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    12.4.5.3. Violin plot -
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7,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,132,134,135,136,138,139,142,143,144,147,148,150,152,153,156,157,161,162,167,168,173,175,178,179,180,194,196,197,199,200,201],afterward:[32,119],ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,11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67,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,183,184],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,134,135,141,142,143,144,145,150,159,161,168,173,175,179,183,184,187,198],consol:122,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,128,129,130,132,133,134,138,143,153,157,169,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Self-paced assignments","40.22. Analyzing COVID-19 papers","40.27. Analyzing data","40.9. Analyzing text about Data Science","40.13. Apply your skills","40.16. Build your own custom vis","40.17. Classifying datasets","40.26. Data preparation","40.24. Data processing in Python","40.41. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","40.40. Data Science project using Azure ML SDK","40.10. Data Science scenarios","40.20. Displaying airport data","40.15. Dive into the beehive","40.23. Estimation of COVID-19 pandemic","40.25. Evaluating data from a form","40.36. Explore a planetary computer dataset","40.37. Exploring for answers","40.19. Introduction to probability and statistics","40.12. Lines, scatters and bars","40.39. Low code/no code Data Science project on Azure ML","40.38. Market research","40.29. Matplotlib applied","40.28. NYC taxi data in winter and summer","40.18. Small diabetes study","40.21. Soda profits","40.35. Tell a story","40.14. Try it in Excel","40.11. Write a data ethics case study","40.101. Intro to Autoencoders","40.102. Base/Denoising Autoencoder & Dimension Reduction","40.103. Fun with Variational Autoencoders","40.90. How to choose cnn architecture mnist","40.94. Object Recognition in Images using CNN","40.92. Sign Language Digits Classification with CNN","40.112. DQN On Foreign Exchange Market","40.113. Art by gan","40.115. Generative Adversarial Networks (GANs)","40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment","40.107. NN Classify 15 Fruits Assignment","40.106. Neural Networks for Classification with TensorFlow","40.116. Basic classification: Classify images of clothing","40.99. Google Stock Price Prediction RNN","40.95. Intro to TensorFlow for Deep Learning","40.104. Time Series Forecasting Assignment","40.83. Counterintuitive Challenges in ML Debugging","40.82. Data engineering","40.84. Case Study: Debugging in Classification","40.85. Case Study: Debugging in Regression","40.75. Beyond random forests: more ensemble models","40.76. Decision trees","40.80. Random Forest Classifier with Feature Importance","40.74. Random forests for classification","40.73. Random forests intro and regression","40.79. Boosting with tuning","40.78. Gradient boosting","40.77. Hyperparameter tuning gradient boosting","40.67. Decision Trees - Classification","40.66. Decision Trees - Intro and Regression","40.63. Kernel method assignment 1","40.65. Support Vector Machines (SVM) - Classification","40.64. Support Vector Machines (SVM) - Intro and SVM for Regression","40.70. Dropout and Batch Normalization","40.71. Lasso and Ridge Regression","40.69. Learning Curve To Identify Overfit & Underfit","40.68. Model selection assignment 1","40.72. Regularized Linear Models","40.88. Build Classification Model","40.87. Build classification models","40.57. Create a regression model","40.61. Delicious asian and indian cuisines","40.62. Explore classification methods","40.55. Exploring visualizations","40.58. Linear and polynomial regression","40.45. Linear regression - California Housing","40.48. Gradient descent","40.49. Linear Regression Implementation from Scratch","40.46. Linear Regression Metrics","40.47. Loss Function","40.54. Managing data","40.50. ML logistic regression - assignment 1","40.51. ML logistic regression - assignment 2","40.52. ML neural network - Assignment 1","40.42. Machine Learning overview - assignment 1","40.43. Machine Learning overview - assignment 2","40.89. Parameter play","40.60. Pumpkin varieties and color","40.53. Regression tools","40.44. Regression with Scikit-learn","40.59. Retrying some regression","40.86. Study the solvers","40.56. Try a different model","40.8. Python programming advanced","40.7. Python programming basics","40.6. Python programming introduction","40.5. Project Plan\u200b Template","40.3. First assignment","40.4. Second assignment","8. Data Science in the cloud","8.1. Introduction","8.3. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","8.2. The \u201clow code/no code\u201d way","9. Data Science in the real world","7.2. Analyzing","7.3. Communication","7. Data Science lifecycle","7.1. Introduction to the Data Science lifecycle","6. Data visualization","6.4. Making meaningful visualizations","6.1. Visualizing distributions","6.2. Visualizing proportions","6.3. Visualizing relationships: all about honey \ud83c\udf6f","4.2. Data Science ethics","4.3. Defining data","4.1. Defining data science","4. Introduction","4.4. Introduction to statistics and probability","5.5. Data preparation","5.2. Non-relational data","5.3. NumPy","5.4.3. Advanced Pandas Techniques","5.4.2. Data Selection","5.4.1. Introduction and Data Structures","5.4. Pandas","5.1. Relational databases","5. Working with data","27. Autoencoder","24. Convolutional Neural Networks","24.3.1.2. Deepdream in TensorFlow","24.3.1.1. Stylenet / Neural-Style","33. Diffusion Model","23. Intro to Deep Learning","30. Deep Q-learning","25. Generative adversarial networks","31. Image classification","32. Image segmentation","28. Long-short term memory","34. Object detection","26. Recurrent Neural Networks","29. Time series","Learn AI together, for free","37. Data engineering","39. Model deployment","38. Model training & evaluation","35. Overview","36. Problem framing","16. Clustering models for Machine Learning","16.1. Introduction to clustering","16.2. K-Means clustering","17.1. Bagging","17.3. Feature importance","17. Getting started with ensemble learning","17.2. Random forest","18.1. Gradient Boosting","18.2. Gradient boosting example","18. Introduction to Gradient Boosting","18.3. XGBoost","18.4. XGBoost + k-fold CV + Feature Importance","20. Kernel method","22. Model selection","19. Unsupervised learning","21. Unsupervised learning: PCA and clustering","13. Build a web app to use a Machine Learning model","14.4. Applied Machine Learning : build a web app","14. 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Build an machine learning web application","41.12. Linear Regression","41.13. Logistic Regression","41.14. Logistic Regression","41.11. Machine Learning overview","41.15. Neural Network","41.3. Python programming advanced","41.2. Python programming basics","41.1. Python programming 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109,110,112,113,114,116,117,118,119,120,121,122,124,126,127,132,133,134,135,137,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,132,134,135,136,138,139,142,143,144,147,148,150,152,153,156,157,161,162,167,168,173,175,178,179,180,194,196,197,199,20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4,147,149,152,154,157,159,161,168,170,173,178,179,180,186,191,193,194],comedi:180,comfort:[7,46,54,117,177],comma:[93,119,167,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,183,184],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,134,135,141,142,143,144,145,150,159,161,168,173,175,179,183,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Self-paced assignments","40.22. Analyzing COVID-19 papers","40.27. Analyzing data","40.9. Analyzing text about Data Science","40.13. Apply your skills","40.16. Build your own custom vis","40.17. Classifying datasets","40.26. Data preparation","40.24. Data processing in Python","40.41. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","40.40. Data Science project using Azure ML SDK","40.10. Data Science scenarios","40.20. Displaying airport data","40.15. Dive into the beehive","40.23. Estimation of COVID-19 pandemic","40.25. Evaluating data from a form","40.36. Explore a planetary computer dataset","40.37. Exploring for answers","40.19. Introduction to probability and statistics","40.12. Lines, scatters and bars","40.39. Low code/no code Data Science project on Azure ML","40.38. Market research","40.29. Matplotlib applied","40.28. NYC taxi data in winter and summer","40.18. Small diabetes study","40.21. Soda profits","40.35. Tell a story","40.14. Try it in Excel","40.11. Write a data ethics case study","40.101. Intro to Autoencoders","40.102. Base/Denoising Autoencoder & Dimension Reduction","40.103. Fun with Variational Autoencoders","40.90. How to choose cnn architecture mnist","40.94. Object Recognition in Images using CNN","40.92. Sign Language Digits Classification with CNN","40.112. DQN On Foreign Exchange Market","40.113. Art by gan","40.115. Generative Adversarial Networks (GANs)","40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment","40.107. NN Classify 15 Fruits Assignment","40.106. Neural Networks for Classification with TensorFlow","40.116. Basic classification: Classify images of clothing","40.99. Google Stock Price Prediction RNN","40.95. Intro to TensorFlow for Deep Learning","40.104. Time Series Forecasting Assignment","40.83. Counterintuitive Challenges in ML Debugging","40.82. Data engineering","40.84. Case Study: Debugging in Classification","40.85. Case Study: Debugging in Regression","40.75. Beyond random forests: more ensemble models","40.76. Decision trees","40.80. Random Forest Classifier with Feature Importance","40.74. Random forests for classification","40.73. Random forests intro and regression","40.79. Boosting with tuning","40.78. Gradient boosting","40.77. Hyperparameter tuning gradient boosting","40.67. Decision Trees - Classification","40.66. Decision Trees - Intro and Regression","40.63. Kernel method assignment 1","40.65. Support Vector Machines (SVM) - Classification","40.64. Support Vector Machines (SVM) - Intro and SVM for Regression","40.70. Dropout and Batch Normalization","40.71. Lasso and Ridge Regression","40.69. Learning Curve To Identify Overfit & Underfit","40.68. Model selection assignment 1","40.72. Regularized Linear Models","40.88. Build Classification Model","40.87. Build classification models","40.57. Create a regression model","40.61. Delicious asian and indian cuisines","40.62. Explore classification methods","40.55. Exploring visualizations","40.58. Linear and polynomial regression","40.45. Linear regression - California Housing","40.48. Gradient descent","40.49. Linear Regression Implementation from Scratch","40.46. Linear Regression Metrics","40.47. Loss Function","40.54. Managing data","40.50. ML logistic regression - assignment 1","40.51. ML logistic regression - assignment 2","40.52. ML neural network - Assignment 1","40.42. Machine Learning overview - assignment 1","40.43. Machine Learning overview - assignment 2","40.89. Parameter play","40.60. Pumpkin varieties and color","40.53. Regression tools","40.44. Regression with Scikit-learn","40.59. Retrying some regression","40.86. Study the solvers","40.56. Try a different model","40.8. Python programming advanced","40.7. Python programming basics","40.6. Python programming introduction","40.5. Project Plan\u200b Template","40.3. First assignment","40.4. Second assignment","8. Data Science in the cloud","8.1. Introduction","8.3. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","8.2. The \u201clow code/no code\u201d way","9. Data Science in the real world","7.2. Analyzing","7.3. Communication","7. Data Science lifecycle","7.1. Introduction to the Data Science lifecycle","6. Data visualization","6.4. Making meaningful visualizations","6.1. Visualizing distributions","6.2. Visualizing proportions","6.3. Visualizing relationships: all about honey \ud83c\udf6f","4.2. Data Science ethics","4.3. Defining data","4.1. Defining data science","4. Introduction","4.4. Introduction to statistics and probability","5.5. Data preparation","5.2. Non-relational data","5.3. NumPy","5.4.3. Advanced Pandas Techniques","5.4.2. Data Selection","5.4.1. Introduction and Data Structures","5.4. Pandas","5.1. Relational databases","5. Working with data","27. Autoencoder","24. Convolutional Neural Networks","24.3.1.2. Deepdream in TensorFlow","24.3.1.1. Stylenet / Neural-Style","33. Diffusion Model","23. Intro to Deep Learning","30. Deep Q-learning","25. Generative adversarial networks","31. Image classification","32. Image segmentation","28. Long-short term memory","34. Object detection","26. Recurrent Neural Networks","29. Time series","Learn AI together, for free","37. Data engineering","39. Model deployment","38. Model training & evaluation","35. Overview","36. Problem framing","16. Clustering models for Machine Learning","16.1. Introduction to clustering","16.2. K-Means clustering","17.1. Bagging","17.3. Feature importance","17. Getting started with ensemble learning","17.2. Random forest","18.1. Gradient Boosting","18.2. Gradient boosting example","18. Introduction to Gradient Boosting","18.3. XGBoost","18.4. XGBoost + k-fold CV + Feature Importance","20. Kernel method","22. Model selection","19. Unsupervised learning","21. Unsupervised learning: PCA and clustering","13. Build a web app to use a Machine Learning model","14.4. Applied Machine Learning : build a web app","14. 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Build an machine learning web application","41.12. Linear Regression","41.13. Logistic Regression","41.14. Logistic Regression","41.11. Machine Learning overview","41.15. Neural Network","41.3. Python programming advanced","41.2. Python programming basics","41.1. Python programming 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