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z8h)_N-9C-z_mZV7fYZM3h@LMC5&WUCFoe-w>s_u~lKUSMLh~5a-^r*nM5IQtai*8% zJ~7TuZTG&OsmJh=>*-iQ|0*ZCW6YY8RlG+`S1iU+9?st2)pry-fig8oJL-$ag-_Ka z^=;i@$cP^2DZx*=EPtF5CgW#{+CD#0=*c90MT{6p-?=N`8{)x_-VswJ=(zZ+cr

  • Control output is the agent’s action: \(u_k = \pi(x_k|K_p, K_i, K_d)\).

    • -
    -../_images/03_policy_example.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png
    -

    Fig. 27.2 Classical PID control loop with scalar quantities#

    +

    Fig. 27.2 Classical PID control loop with scalar quantities#

    @@ -2005,18 +1999,18 @@

    27.3. Main algorithms -../_images/04_maze.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png
    -

    Fig. 27.3 Maze setup statement#

    +

    Fig. 27.3 Maze setup statement#

    27.3.1. RL-solution by policy#

    -
    -../_images/05_maze_policy.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png
    -

    Fig. 27.4 Maze solved by policy#

    +

    Fig. 27.4 Maze solved by policy#

    Key characteristics:

    @@ -2027,10 +2021,10 @@

    27.3.1. RL-solution by policy

    27.3.2. RL-solution by value function#

    -
    -../_images/06_maze_valuefunc.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png
    -

    Fig. 27.5 Maze solved by value function#

    +

    Fig. 27.5 Maze solved by value function#

    Key characteristics:

    @@ -2041,10 +2035,10 @@

    27.3.2. RL-solution by value function

    27.3.3. RL-solution by model evaluation#

    -
    -../_images/07_maze_modeleval.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png
    -

    Fig. 27.6 Maze solved by model evaluation#

    +

    Fig. 27.6 Maze solved by model evaluation#

    Key characteristics:

    @@ -2056,10 +2050,10 @@

    27.3.3. RL-solution by model evaluation<

    27.3.4. RL agent taxonomy#

    -
    -../_images/07_taxonomy.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png
    -

    Fig. 27.7 Main categories of reinforcement learning algorithms#

    +

    Fig. 27.7 Main categories of reinforcement learning algorithms#

    @@ -2069,17 +2063,38 @@

    27.4. Code
    -
    import tensorflow as tf
+
    import tensorflow as tf
 import random as r
 import numpy as np
 import matplotlib.pyplot as plt
    +
    +
    WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002B94E742100>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/pandas/
+  WARNING: Failed to remove contents in a temporary directory 'C:\Users\yienk\anaconda3\envs\tensorflow\Lib\site-packages\~mq'.
+  You can safely remove it manually.
+
    +
    +
    -
    class Maze:
+
    class NeuralNetwork:
+    def __init__(self, input_size, num_memory_units):
+        self.model = self.create_model(input_size, num_memory_units)
+        
+    def create_model(self, input_size, num_memory_units):
+        model = tf.keras.models.Sequential([
+            tf.keras.layers.Dense(6, activation='sigmoid', input_shape=(input_size,)),
+            tf.keras.layers.Dense(6, activation='sigmoid'),
+            tf.keras.layers.Dense(4 + num_memory_units)
+        ])
+        return model
+    
+    def predict(self, inputs):
+        return self.model(inputs, training=True)
+class Maze:
     def __init__(self, maze_type):
         # Use the maze types from maze_collection.py
         [self.maze,
@@ -2212,6 +2227,9 @@ 
    27.4. Codenum_memory_units = 4
     graphical = True
     file_output = True
+    nn = NeuralNetwork(2 + num_memory_units, num_memory_units)
+    
+    optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
 
     if file_output is True:
         # output to file (this is set to overwrite!)
@@ -2249,6 +2267,31 @@ 
    27.4. Code# Looping through iterations
     while iteration < maxIterations:
+        with tf.GradientTape() as tape:
+            y = nn.predict(tf.convert_to_tensor([input_values], dtype=tf.float32))
+            left_output = y[:, 0:4]
+            right_output = y[:, 4:]
+            action_probs = tf.nn.softmax(left_output)
+            memory_units = tf.sigmoid(right_output)
+            action = np.random.choice(4, p=action_probs[0].numpy())
+
+            if action == 0:
+                m.move_up()
+                dp_dthetas.append(tape.gradient(action_probs[0, 0], nn.model.trainable_variables))
+            elif action == 1:
+                m.move_right()
+                dp_dthetas.append(tape.gradient(action_probs[0, 1], nn.model.trainable_variables))
+            elif action == 2:
+                m.move_down()
+                dp_dthetas.append(tape.gradient(action_probs[0, 2], nn.model.trainable_variables))
+            else:
+                m.move_left()
+                dp_dthetas.append(tape.gradient(action_probs[0, 3], nn.model.trainable_variables))
+        for i in range(step):
+            grads = dp_dthetas[i]
+            delta = [(learning_rate * (1 / action_probs[0, action].numpy()) * reward) * grad for grad in grads]
+            optimizer.apply_gradients(zip(delta, nn.model.trainable_variables))
+
         # Current step
         step = 0
 
@@ -2397,12 +2440,91 @@ 
    27.4. Code
 
    
-
    if __name__ == "__main__":
-    import os
+
    import os
+import tensorflow as tf
+
+# Ensure we are using TensorFlow 2.x
+if not tf.__version__.startswith('2'):
+    raise ValueError('This code requires TensorFlow V2.x')
+
+def main(directory_name):
+    # Sample TensorFlow operation for demonstration
+    # This creates a constant tensor and prints it.
+    tensor = tf.constant([[1, 2], [3, 4]])
+    print(f"In directory: {directory_name}, tensor values are:")
+    print(tensor)
+
+if __name__ == "__main__":
     for run in range(11, 25):
         number = "{:05d}".format(run)
-        os.mkdir("T_fixed-memory-linear_reward_" + number)
-        main("T_fixed-memory-linear_reward_" + number)
+        dir_name = "T_fixed-memory-linear_reward_" + number
+        
+        # Check if directory exists, if not create it
+        if not os.path.exists(dir_name):
+            os.mkdir(dir_name)
+        
+        # Execute main function
+        main(dir_name)
+
    
+
    
+
    
+
    
+
    In directory: T_fixed-memory-linear_reward_00011, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00012, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00013, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00014, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00015, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00016, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00017, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00018, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00019, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00020, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00021, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00022, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00023, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
+In directory: T_fixed-memory-linear_reward_00024, tensor values are:
+tf.Tensor(
+[[1 2]
+ [3 4]], shape=(2, 2), dtype=int32)
 
    
 
    
 
    
diff --git a/ml-advanced/ensemble-learning/bagging.html b/ml-advanced/ensemble-learning/bagging.html
index a6bee70df5..a870460022 100644
--- a/ml-advanced/ensemble-learning/bagging.html
+++ b/ml-advanced/ensemble-learning/bagging.html
@@ -1326,7 +1326,7 @@ 
    Ocademy Open Machine Learning Book
    
   
    
     
    
       
       
       
  • 
-        Ocademy Open Machine Learning Book
    • 
       
       
       
  • 
-        Ocademy Open Machine Learning Book
    • 
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
-title="Download notebook file"
->
-  
-
-
-  
-  
-.ipynb
-
-
-      
-      
-      
  • 
-        
   
@@ -1495,7 +1478,7 @@ 
    Ocademy Open Machine Learning Book
    
 
   
   
-.md
+.ipynb
     
 
       
    • 
@@ -1632,7 +1615,18 @@ 
     Contents 
    
                 
               
    
                 
-  
    
+  
    
+
    
+
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython 
+
    
+
    
+
    
+
    
+
    
 
    15.1. Bagging#
    
 
    In previous sections, we explored different classification algorithms as well as techniques that can be used to properly validate and evaluate the quality of your models.
    
 
    Now, suppose that we have chosen the best possible model for a particular problem and are struggling to further improve its accuracy. In this case, we would need to apply some more advanced machine learning techniques that are collectively referred to as ensembles.
    
@@ -1661,10 +1655,10 @@ 
    15.1.2. Bootstrapping\(\large X\) of size \(\large N\). We can make a new sample from the original sample by drawing \(\large N\) elements from the latter randomly and uniformly, with replacement. In other words, we select a random element from the original sample of size \(\large N\) and do this \(\large N\) times. All elements are equally likely to be selected, thus each element is drawn with the equal probability \(\large \frac{1}{N}\).
    
 
    Let’s say we are drawing balls from a bag one at a time. At each step, the selected ball is put back into the bag so that the next selection is made equiprobably i.e. from the same number of balls \(\large N\). Note that, because we put the balls back, there may be duplicates in the new sample. Let’s call this new sample \(\large X_1\).
    
 
    By repeating this procedure \(\large M\) times, we create \(\large M\) bootstrap samples \(\large X_1, \dots, X_M\). In the end, we have a sufficient number of samples and can compute various statistics of the original distribution.
    
-
    
-https://habrastorage.org/webt/n0/dg/du/n0dgduav1ygc3iylumtwjcn15mu.png
+
    
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png
 
    
-
    Fig. 15.1 Process of bootstrap#
    
+
    Fig. 15.1 Process of bootstrap#
    
 
    
 
    
 
    For our example, we’ll use the familiar telecom_churn dataset. Previously, when we discussed feature importance, we saw that one of the most important features in this dataset is the number of calls to customer service. Let’s visualize the data and look at the distribution of this feature.
    
@@ -1674,17 +1668,21 @@ 
    15.1.2. Bootstrappingimport pandas as pd
 import seaborn as sns
 from matplotlib import pyplot as plt
-
-
-# Graphics in retina format are more sharp and legible
+
    
+
    
+
    +
    +
    +
    +
    # Graphics in retina format are more sharp and legible
 %config InlineBackend.figure_format = 'retina'
    -
    +
    -
    telecom_data = pd.read_csv("../../assets/data/telecom_churn.csv")
+
    telecom_data = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/telecom_churn.csv")
 
 telecom_data.head()
 
    
@@ -1861,12 +1859,15 @@ 
    15.1.2. Bootstrapping)
 plt.xlabel("Number of calls")
 plt.ylabel("Density")
-plt.legend();
+plt.legend()
    -../../_images/bagging_3_0.png +
    <matplotlib.legend.Legend at 0x7f10144ed250>
+
    +
    +../../_images/bagging_9_1.png

    Looks like loyal customers make fewer calls to customer service than those who eventually leave. Now, it might be a good idea to estimate the average number of customer service calls in each group. Since our dataset is small, we would not get a good estimate by simply calculating the mean of the original sample. We will be better off applying the bootstrap method. Let’s generate 1000 new bootstrap samples from our original population and produce an interval estimate of the mean.

    @@ -1883,9 +1884,13 @@

    15.1.2. Bootstrapping """Produce an interval estimate.""" boundaries = np.percentile(stat, [100 * alpha / 2.0, 100 * (1 - alpha / 2.0)]) return boundaries - - -# Save the data about the loyal and former customers to split the dataset +

    +
    +
    +
    +
    +
    +
    # Save the data about the loyal and former customers to split the dataset
 loyal_calls = telecom_data.loc[
     telecom_data["Churn"] == False, "Customer service calls"
 ].values
@@ -1929,10 +1934,10 @@ 
    15.1.3. BaggingSuppose that we have a training set \(\large X\). Using bootstrapping, we generate samples \(\large X_1, \dots, X_M\). Now, for each bootstrap sample, we train its own classifier \(\large a_i(x)\). The final classifier will average the outputs from all these individual classifiers. In the case of classification, this technique corresponds to voting:
 $\(\large a(x) = \frac{1}{M}\sum_{i = 1}^M a_i(x).\)$
    
 
    The picture below illustrates this algorithm:
    
-
    
-https://habrastorage.org/webt/3t/ab/xy/3tabxy0j8tqqkctkc9f-89ep780.png
+
    
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png
 
    
-
    Fig. 15.2 Illustration of Bagging#
    
+
    Fig. 15.2 Illustration of Bagging#
    
 
    
 
    
 
    Let’s consider a regression problem with base algorithms \(\large b_1(x), \dots , b_n(x)\). Assume that there exists an ideal target function of true answers \(\large y(x)\) defined for all inputs and that the distribution \(\large p(x)\) is defined. We can then express the error for each regression function as follows:
    
@@ -1969,10 +1974,10 @@ 
    15.1.3. BaggingBagging reduces the variance of a classifier by decreasing the difference in error when we train the model on different datasets. In other words, bagging prevents overfitting. The efficiency of bagging comes from the fact that the individual models are quite different due to the different training data and their errors cancel each other out during voting. Additionally, outliers are likely omitted in some of the training bootstrap samples.
    
 
    The scikit-learn library supports bagging with meta-estimators BaggingRegressor and BaggingClassifier. You can use most of the algorithms as a base.
    
 
    Let’s examine how bagging works in practice and compare it with a decision tree. For this, we will use an example from sklearn’s documentation.
    
-
    
-https://habrastorage.org/webt/9l/gv/_o/9lgv_outzpteptakegjbpyfp2ue.png
+
    
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png
 
    
-
    Fig. 15.3 Comparison of Bagging and Tree#
    
+
    Fig. 15.3 Comparison of Bagging and Tree#
    
 
    
 
    
 
    The error for the decision tree:
    
@@ -1991,10 +1996,10 @@ 
    15.1.4. Out of bag error\(\large k\)-th tree.
    
 
    This is easy to prove. Suppose there are \(\large \ell\) examples in our dataset. At each step, each data point has equal probability of ending up in a bootstrap sample with replacement, probability \(\large\frac{1}{\ell}.\) The probability that there is no such bootstrap sample that contains a particular dataset element (i.e. it has been omitted \(\large \ell\) times) equals \(\large (1 - \frac{1}{\ell})^\ell\). When \(\large \ell \rightarrow +\infty\), it becomes equal to the Second Remarkable Limit \(\large \frac{1}{e}\). Then, the probability of selecting a specific example is \(\large \approx  1 - \frac{1}{e} \approx 63\%\).
    
 
    Let’s visualize how Out-of-Bag Error (or OOBE) estimation works:
    
-
    
-https://habrastorage.org/webt/mt/zr/ox/mtzroxoyxu0xcpmsbslgwbs1vs8.png
+
    
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png
 
    
-
    Fig. 15.4 Principle of OOBE#
    
+
    Fig. 15.4 Principle of OOBE#
    
 
    
 
    
 
    The top part of the figure above represents our original dataset. We split it into the training (left) and test (right) sets. In the left image, we draw a grid that perfectly divides our dataset according to classes. Now, we use the same grid to estimate the share of the correct answers on our test set. We can see that our classifier gave incorrect answers in those 4 cases that have not been used during training (on the left). Hence, the accuracy of our classifier is \(\large \frac{11}{15}*100\% = 73.33\%\).
    
diff --git a/ml-advanced/ensemble-learning/feature-importance.html b/ml-advanced/ensemble-learning/feature-importance.html
index 0a46e4db91..7daa29b348 100644
--- a/ml-advanced/ensemble-learning/feature-importance.html
+++ b/ml-advanced/ensemble-learning/feature-importance.html
@@ -1326,7 +1326,7 @@ 
    Ocademy Open Machine Learning Book
    
   
    
     
    
       
       
       
  • 
-        Ocademy Open Machine Learning Book
    • 
       
       
       
  • 
-        Ocademy Open Machine Learning Book
    • 
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
-title="Download notebook file"
->
-  
-
-
-  
-  
-.ipynb
-
-
-      
-      
-      
  • 
-        
   
@@ -1495,7 +1478,7 @@ 
    Ocademy Open Machine Learning Book
    
 
   
   
-.md
+.ipynb
     
 
       
    • 
@@ -1646,16 +1629,27 @@ 
     Contents 
    
                 
               
    
                 
-  
    
+  
    
+
    
+
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython
+
    
+
    
+
    
+
    
+
    
 
    15.3. Feature importance#
    
 
    It’s quite often that you want to make out the exact reasons of the algorithm outputting a particular answer. Or at the very least to find out which input features contributed most to the result. With Random Forest, you can obtain such information quite easily.
    
 
    
 
    15.3.1. Intuition#
    
 
    From the picture below, it is intuitively clear that, in our credit scoring problem, Age is much more important than Income. This can be formally explained using the concept of information gain.
    
-
    
-https://habrastorage.org/webt/va/mm/9l/vamm9luoz1oqa4op74egpttaggy.png
+
    
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png
 
    
-
    Fig. 15.7 Example of credit scoring problem#
    
+
    Fig. 15.7 Example of credit scoring problem#
    
 
    
 
    
 
    In the case of many decision trees or a random forest, the closer the mean position of a feature over all the trees to the root, the more significant it is for a given classification or regression problem. Gains in the splitting criterion, such as the Gini impurity, obtained at each optimal split in every tree is a measure of importance that is directly associated with the splitting feature. The value of this score is distinct for each feature and accumulates over all the trees.
    
@@ -1781,13 +1775,18 @@ 
    15.3.3. Sklearn Random Forest Feature Im
 import numpy as np
 import pandas as pd
 import seaborn as sns
-
-iris_data = pd.read_csv("../../assets/data/iris.csv")
    -
    +
    +
    +
    iris_data = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/iris.csv")
+
    +
    +
    +
    +
    data = iris_data.iloc[:, :-1]
 data.head()
@@ -1921,7 +1920,7 @@ 
    15.3.3. Sklearn Random Forest Feature Im
    -../../_images/feature-importance_10_0.png +../../_images/feature-importance_18_0.png
    @@ -1938,7 +1937,7 @@

    15.3.3. Sklearn Random Forest Feature Im

    -../../_images/feature-importance_11_0.png +../../_images/feature-importance_19_0.png
    @@ -1955,7 +1954,7 @@

    15.3.3. Sklearn Random Forest Feature Im

    -../../_images/feature-importance_12_0.png +../../_images/feature-importance_20_0.png

    Let’s start from the first tree and Sepal length (cm) feature. This feature is located in two nodes: the root (#0) and the rightmost node (#4). The reduction in impurity for these nodes are:

    @@ -2018,7 +2017,7 @@

    15.3.4. Practical example 
    from sklearn.ensemble import RandomForestRegressor
 
-hostel_data = pd.read_csv("../../assets/data/hostel_factors.csv")
+hostel_data = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/hostel_factors.csv")
 features = {
     "f1": u"Staff",
     "f2": u"Hostel booking",
@@ -2078,7 +2077,7 @@ 
    15.3.4. Practical example
+../../_images/feature-importance_24_1.png

    The picture above shows that, more often than not, customers pay great attention to staff and the price-quality ratio. This couple of factors affects the resulting overall rating the most. The difference between these two features and other features is not very large, so we can conclude that exclusion of any of these features will lead to a reduction of model’s accuracy. However, based on our analysis, we can recommend hostel owners to focus primarily on staff training and price-to-quality ratio.

    diff --git a/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html b/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html index ab210d0632..4aa5ca3faf 100644 --- a/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html +++ b/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html @@ -1324,7 +1324,7 @@

    Ocademy Open Machine Learning Book

  • - Ocademy Open Machine Learning Book

  • - Ocademy Open Machine Learning Book

    • class="headerbtn" data-toggle="tooltip" data-placement="left" -title="Download notebook file" -> - - - - - -.ipynb - - - - -
  • - @@ -1493,7 +1476,7 @@

    Ocademy Open Machine Learning Book

    -.md +.ipynb
    • @@ -1543,11 +1526,37 @@

    Getting started with ensemble learning

    -
    +
    +
    +
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython
+
    +
    +
    +
    +

    15. Getting started with ensemble learning#

    +
    +
    +
    from IPython.display import HTML
+display(HTML("""
+<div class="yt-container">
+   <iframe src="https://www.youtube.com/embed/JcI5E2Ng6r4" allowfullscreen></iframe>
+</div>
+"""))
+
    +
    +
    +
    +
    +
    +
    • 15.1. Bagging
      • diff --git a/ml-advanced/ensemble-learning/random-forest.html b/ml-advanced/ensemble-learning/random-forest.html index 8477c9b30b..aca1e5c680 100644 --- a/ml-advanced/ensemble-learning/random-forest.html +++ b/ml-advanced/ensemble-learning/random-forest.html @@ -1326,7 +1326,7 @@

      Ocademy Open Machine Learning Book

      • - Ocademy Open Machine Learning Book
      • - Ocademy Open Machine Learning Book
      • - Ocademy Open Machine Learning Book class="headerbtn" data-toggle="tooltip" data-placement="left" -title="Download notebook file" -> - - - - - -.ipynb - - -
        • - -
      • - @@ -1495,7 +1478,7 @@

        Ocademy Open Machine Learning Book

        -.md +.ipynb
        • @@ -1720,7 +1703,18 @@

        Contents

        -
        +
        +
        +
        # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython
+
        +
        +
        +
        +

        15.2. Random forest#

        Leo Breiman managed to apply bootstrapping not only in statistics but also in machine learning. He, along with Adel Cutler, extended and improved the random forest algorithm proposed by Tin Kam Ho. They combined the construction of uncorrelated trees using CART, bagging, and the random subspace method.

        Decision trees are a good choice for the base classifier in bagging because they are quite sophisticated and can achieve zero classification error on any sample. The random subspace method reduces the correlation between the trees and thus prevents overfitting. With bagging, the base algorithms are trained on different random subsets of the original feature set.

        @@ -1733,18 +1727,45 @@

        15.2. Random forest

        Train each model.

      • Apply the resulting ensemble model to a new instance by combining the results from all the models in \(\large L\). You can use either majority voting or aggregation of the posterior probabilities.

        • -

        - -A demo of Decision Tree. [source] -

        -

        - -A demo of Decision Tree. [source] -

        -

        - -A demo of Decision Tree. [source] -

        +
        +
        +
        from IPython.display import HTML
+display(HTML("""
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/random-forest/index.html" width="105%" height="700px;" style="border:none;" scrolling="yes"></iframe>
+A demo of Decision Tree. <a href="https://mlu-explain.github.io/random-forest/">[source]</a>
+</p>
+"""))
+
        +
        +
        +
        +
        +
        +
        from IPython.display import HTML
+display(HTML("""
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/random-forest-2/index.html" width="105%" height="1000px;" style="border:none;" scrolling="no"></iframe>
+A demo of Decision Tree. <a href="https://cs.stanford.edu/~karpathy/svmjs/demo/demoforest.html">[source]</a>
+</p>
+"""))
+
        +
        +
        +
        +
        +
        +
        from IPython.display import HTML
+display(HTML("""
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/random-forest-3/index.html" width="110%" height="600px;" style="border:none;" scrolling="no"></iframe>
+A demo of Decision Tree. <a href="https://nlhlong01.github.io/playground/#dataset=circle&regDataset=reg-plane&noise=25&seed=0.59759&showTestData=false&discretize=false&percTrainData=60&problem=classification&percSamples=50&nTrees=100&maxDepth=5">[source]</a>
+</p>
+"""))
+
        +
        +
        +

        15.2.1. Algorithm#

        The algorithm for constructing a random forest of \(\large N\) trees goes as follows:

        @@ -1772,19 +1793,19 @@

        15.2.1. Algorithm15.2.2. Comparison with Decision Trees and Bagging#

        -
        # Disable warnings in Anaconda
+
        # Disable warnings in Anaconda
 import warnings
 
 import numpy as np
 
 warnings.filterwarnings('ignore')
 
-%matplotlib inline
+%matplotlib inline
 from matplotlib import pyplot as plt
 
 plt.style.use('ggplot')
 plt.rcParams['figure.figsize'] = 10, 6
-%config InlineBackend.figure_format = 'retina'
+%config InlineBackend.figure_format = 'retina'
 
 import seaborn as sns
 from sklearn.datasets import make_circles
@@ -1798,7 +1819,7 @@ 
        15.2.2. Comparison with Decision Trees a
 
        
 
        
 
        
-
        n_train = 150        
+
        n_train = 150        
 n_test = 1000       
 noise = 0.1
 
@@ -1818,8 +1839,13 @@ 
        15.2.2. Comparison with Decision Trees a
 
 X_train, y_train = generate(n_samples=n_train, noise=noise)
 X_test, y_test = generate(n_samples=n_test, noise=noise)
-
-# One decision tree regressor
+
        
+
        
+
        
+
        
+
        
+
        
+
        # One decision tree regressor
 dtree = DecisionTreeRegressor().fit(X_train, y_train)
 d_predict = dtree.predict(X_test)
 
@@ -1828,10 +1854,14 @@ 
        15.2.2. Comparison with Decision Trees a
 plt.scatter(X_train, y_train, c="b", s=20)
 plt.plot(X_test, d_predict, "g", lw=2)
 plt.xlim([-5, 5])
-plt.title("Decision tree, MSE = %.2f" 
-          % np.sum((y_test - d_predict) ** 2))
-
-# Bagging with a decision tree regressor
+plt.title("Decision tree, MSE = %.2f" % np.sum((y_test - d_predict) ** 2))
+
        
+
        
+
        
+
        
+
        
+
        
+
        # Bagging with a decision tree regressor
 bdt = BaggingRegressor(DecisionTreeRegressor()).fit(X_train, y_train)
 bdt_predict = bdt.predict(X_test)
 
@@ -1841,8 +1871,13 @@ 
        15.2.2. Comparison with Decision Trees a
 plt.plot(X_test, bdt_predict, "y", lw=2)
 plt.xlim([-5, 5])
 plt.title("Bagging for decision trees, MSE = %.2f" % np.sum((y_test - bdt_predict) ** 2));
-
-# Random Forest
+
        
+
        
+
        
+
        
+
        
+
        
+
        # Random Forest
 rf = RandomForestRegressor(n_estimators=10).fit(X_train, y_train)
 rf_predict = rf.predict(X_test)
 
@@ -1860,7 +1895,7 @@ 
        15.2.2. Comparison with Decision Trees a
 
        We can also look at the advantages of random forests and bagging in classification problems:
        
 
        
 
        
-
        np.random.seed(42)
+
        np.random.seed(42)
 X, y = make_circles(n_samples=500, factor=0.1, noise=0.35, random_state=42)
 X_train_circles, X_test_circles, y_train_circles, y_test_circles = \
     train_test_split(X, y, test_size=0.2)
@@ -1876,8 +1911,13 @@ 
        15.2.2. Comparison with Decision Trees a
 plt.scatter(X[:,0], X[:,1], c=y, cmap='viridis', alpha=.7)
 plt.title("Decision tree")
 plt.show()
-
-b_dtree = BaggingClassifier(DecisionTreeClassifier(), 
+
        
+
        
+
        
+
        
+
        
+
        
+
        b_dtree = BaggingClassifier(DecisionTreeClassifier(), 
                             n_estimators=300, random_state=42)
 b_dtree.fit(X_train_circles, y_train_circles)
 
@@ -1889,8 +1929,13 @@ 
        15.2.2. Comparison with Decision Trees a
 plt.scatter(X[:,0], X[:,1], c=y, cmap='viridis', alpha=.7)
 plt.title("Bagging (decision trees)")
 plt.show()
-
-rf = RandomForestClassifier(n_estimators=300, random_state=42)
+
        
+
        
+
        
+
        
+
        
+
        
+
        rf = RandomForestClassifier(n_estimators=300, random_state=42)
 rf.fit(X_train_circles, y_train_circles)
 
 x_range = np.linspace(X.min(), X.max(), 100)
@@ -1948,13 +1993,13 @@ 
        15.2.3.1. Practice with random forests i
 
        First, let’s build a simple classifier which we will use as a baseline. For the sake of simplicity, we will use only numeric features.
        
 
        
 
        
-
        import pandas as pd
+
        import pandas as pd
 from sklearn.metrics import accuracy_score
 from sklearn.model_selection import (GridSearchCV, StratifiedKFold,
                                      cross_val_score)
 
 # Load data
-df = pd.read_csv("../../assets/data/telecom_churn.csv")
+df = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/telecom_churn.csv")
 
 # Choose the numeric features
 cols = []
@@ -1984,7 +2029,7 @@ 
        15.2.3.1. Practice with random forests i
 
        Let’s start with the number of trees:
        
 
        
 
        
-
        # Initialize the validation
+
        # Initialize the validation
 skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
 
 # Create lists to save the values of accuracy on training and test sets
@@ -2016,7 +2061,7 @@ 
        15.2.3.1. Practice with random forests i
 
        
 
        
 
        
-
        plt.style.use('ggplot')
+
        plt.style.use('ggplot')
 
 fig, ax = plt.subplots(figsize=(8, 4))
 ax.plot(trees_grid, train_acc.mean(axis=1), alpha=0.5, color='blue', label='train')
@@ -2038,7 +2083,7 @@ 
        15.2.3.1. Practice with random forests i
 
        We will start with the maximum depth of trees max_depth and fix the number of trees at 100:
        
 
        
 
        
-
        # Create lists to save accuracy values on the training and test sets
+
        # Create lists to save accuracy values on the training and test sets
 train_acc = []
 test_acc = []
 temp_train_acc = []
@@ -2081,7 +2126,7 @@ 
        15.2.3.1. Practice with random forests i
 
        Another important parameter worth tuning is min_samples_leaf. It also contributes to regularization.
        
 
        
 
        
-
        # Create lists to save accuracy values on the training and test sets
+
        # Create lists to save accuracy values on the training and test sets
 train_acc = []
 test_acc = []
 temp_train_acc = []
@@ -2111,7 +2156,7 @@ 
        15.2.3.1. Practice with random forests i
 
        
 
        
 
        
-
        fig, ax = plt.subplots(figsize=(8, 4))
+
        fig, ax = plt.subplots(figsize=(8, 4))
 ax.plot(min_samples_leaf_grid, train_acc.mean(axis=1), alpha=0.5, color='blue', label='train')
 ax.plot(min_samples_leaf_grid, test_acc.mean(axis=1), alpha=0.5, color='red', label='cv')
 ax.fill_between(min_samples_leaf_grid, test_acc.mean(axis=1) - test_acc.std(axis=1), 
@@ -2130,7 +2175,7 @@ 
        15.2.3.1. Practice with random forests i
 
        Let’s consider the parameter max_features. For classification, the value \(\large \sqrt{d}\) (the total number of features) is typically used as the default choice. Let’s check whether it would be optimal to use 4 features in our case:
        
 
        
 
        
-
        # Create lists to save accuracy values on the training and test sets
+
        # Create lists to save accuracy values on the training and test sets
 train_acc = []
 test_acc = []
 temp_train_acc = []
@@ -2174,7 +2219,7 @@ 
        15.2.3.1. Practice with random forests i
 
        We have seen how the learning curves change with different values of the basic parameters. Now, let’s use GridSearch to find the optimal parameters for our example:
        
 
        
 
        
-
        # Initialize the set of parameters for exhaustive search and fit 
+
        # Initialize the set of parameters for exhaustive search and fit 
 parameters = {'max_features': [4, 7, 10, 13], 
               'min_samples_leaf': [1, 3, 5, 7], 
               'max_depth': [5, 10, 15, 20]}
@@ -2187,7 +2232,7 @@ 
        15.2.3.1. Practice with random forests i
 
        
 
        
 
        
-
        gcv.best_params_, gcv.best_score_
+
        gcv.best_params_, gcv.best_score_
 
        
 
        
 
        
@@ -2214,10 +2259,10 @@ 
        15.2.4. Variance and DecorrelationIn fact, this conditional correlation is not directly related to the averaging process, and the dependence of \(\large \rho(x)\) on \(\large x\) warns us of this difference. \(\large \rho(x)\) is the theoretical correlation between a pair of random trees estimated on the input \(\large x\). Its value comes from the repeated sampling of the training set from the population \(\large Z\) and the subsequent random choice of a pair of trees. In statistics jargon, this is the correlation caused by the sampling distribution of \(\large Z\) and \(\large \Theta\).
        
 
        The conditional covariance of any pair of trees is equal to 0 because bootstrapping and feature selection are independent and identically distributed.
        
 
        If we consider the variance of a single tree, it barely depends on the parameters of the splitting (\(\large m\)). But they are crucial for ensembles. The variance of a tree is much higher than the one of an ensemble. The book The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani and Jerome Friedman) has a great example that demonstrates this fact:
        
-
        
-https://habrastorage.org/webt/ee/3a/6v/ee3a6vx_gvop_wibdscz38qazba.png
+
        
+https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png
 
        
-
        Fig. 15.5 Comparison of Tree and Random Forest#
        
+
        Fig. 15.5 Comparison of Tree and Random Forest#
        @@ -2252,10 +2297,10 @@

        15.2.7. Similarities between Random Fore

        You can see that the response of a random forest is a weighted sum of responses over all training examples.

        It is also worth noting that the number of the leaf \( \large T_n(x)\), where the instance \(\large x\) ended up, is a valuable feature by itself. For example, the following approach works well: 1) A composition of a small number of trees is trained on a sample using a random forest or gradient boosting. 2) The categorical features \(\large T_1(x), \dots, T_n(x)\) are added to the sample.

        These new features are the result of the non-linear space splitting, and they provide information about similarity between examples. In the book The Elements of Statistical Learning, there is a good illustrative example that demonstrates this similarity between random forests and the k-nearest neighbors technique:

        -
        -https://habrastorage.org/webt/ee/3a/6v/ee3a6vx_gvop_wibdscz38qazba.png +
        +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png
        -

        Fig. 15.6 Similarity between random forests and knn#

        +

        Fig. 15.6 Similarity between random forests and knn#

        diff --git a/ml-advanced/gradient-boosting/xgboost.html b/ml-advanced/gradient-boosting/xgboost.html index 5bb230849e..2a0e7349f0 100644 --- a/ml-advanced/gradient-boosting/xgboost.html +++ b/ml-advanced/gradient-boosting/xgboost.html @@ -1786,7 +1786,7 @@

        16.3.2. Example

        -
        Mean Absolute Error : 19357.02450770548
+
        Mean Absolute Error : 16411.89467037671
        diff --git a/ml-advanced/kernel-method.html b/ml-advanced/kernel-method.html index 164fa8666c..80c5c31bba 100644 --- a/ml-advanced/kernel-method.html +++ b/ml-advanced/kernel-method.html @@ -1913,7 +1913,7 @@

        18.3. Fitting a support vector machine

    @@ -1971,7 +1971,7 @@

    18.4. Beyond linear boundaries: Kernel S

    Next we describe how to select and adjust the radial basis function centres. @@ -2086,7 +2086,7 @@

    18.13. 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"}}]}}, 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"}}]}}, 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0KAuENj0hExOKEEJGMVbdVm+3BaufYEO5WKw0itFYdVvw7XdidVsuN1Ld1pl9AblyJQI93n9tJIYRGIKuWQNQ22S3yWRS+L5ieDg/71Y8EzG3goRE7XVQwfe9mkNDKEgIGU1YgEIaTEfdFolYDQn1nKjbpkAnk82LDwRPTgh0PN5Qt9UpFIqTChLmC3L1Ksy//Q18D+d52xM5/NDG6mS21B0SMpkU5XK10X7bsGGIWCxKOp1geDhPPB5Fa00uV2w4JEgpQ0FCyCjCAhQyI3WbYRgIQWPOZ6S6LZcrIaUglYp3fJh0MpxjjsH+/R1opSbP5alW0IkEseNeipVKNNRt6XSSRCLO8PAcKz49D+uOOzD/9Q+E6+Fvvz3OCSegU+mmD5OrVxH/zKexb/0twnFAa7RponbfHftzn8M+4KARXwRaY6wgYdOmHIsXZxuChP7+DH19KbTWSCmxLBPHcRHCR2uJEOFHTshmQhn2c5hWh0lBY9X2WOZymHTC55TL0feSgxGDmyCRHJfNg+sgqlU480z8r148aph0tg4J7cC86y6SH/sIcuUzm2/UGp3JUH7f+6m8+S3jnxNB6zF98qswVq1GmwbYdnA/zwuer22jLr2UwSOPmfU1NnNI6OtLYlkWQ0M54vFY6JDQY/SaDDssQM9RRrbb6oxst5VK1XHDpKVSFa11oxj5vk8+H7TlolEbpQJ3gG612ybD+uOdpN7+VkSxiDYtsEyoVhHlcuDLZhjovj7cQ15C8QMfQu26a+OxrTgktAvz3ntJvfXM4LqTic1CCt9HFIsAlD7wISpnvXPcY1Nnvhn7tlvRsdj4lZ/WiFIJUinK//gnpcjs3cAnckhQSmFZwQondEjoTcICFDKntGuY1Pd9bNtCa127z+zabcbD/yVy9dXYN/86+ADeYjHV15xM5eTXobfccubHu+8+Yt/8OvbttwUf3p4XpNbUn3j9v5ZF+cz/R+nTnwXDQAhBNpumWnWn5ZDQNrQmc+IJGA/cj+7rm3CVI/J5tB1h6PY70EuWNG6XK1bQd8ShwV8ikYmPr1RQgC+4gKHXnz5rQULtoslm+3Cc4DWTzabxvLpSLlgRBWq4CBs2DNUcEjSGYSNEKEiYC3qtAIUy7OcIs49KSIwaJg3s94MPydkOk0auu5bMK19B9Ec/QG7cgHCqyKeeIvZ/F9B34vGY//rnjI/p77UXxUsuxTvnXIjFwLaD4iNlsLIIhpvA84hdegmJj34ElOpIZMN0MP/+N4yH/4uOxycsPgA6kUCUS0R+duOo261770G4btB2mwwpg+d3xx0diWxIJKKNLyNhZEPIdAkL0HOA9kQl+OOGScvlMlJKpJS02m4z77qLxP/+D1Sr6HQanUqhEwl0JoNOpxHr1wfttHXrZnTcSMQmm4hh/exGtO+D523uO9apV2WtifzkOqw//RFob2TDdDEeejDID5pGETEffHD07U5tv2qqtpYQ+MVS2x0SPM/Htm1yuQJSitAhIWTahAVoAdPpZNJisYzruiQSiZavMXbpd8Fx0On0+A9QKRtFKPrT66Z1PMOQjWFS75570U8/HawOYOIPaCFACES1QuS6axs3T+SQ0Kuo5cuDX7LXZHap1nb0ttuubQ4JUkrS6RSmaTRWP9N1SNA6HE4NCQvQgqTd7bZm3m2BPYskFptk76EJcs2zWPfcjY5EJv/2Xtuwsn/60ymf81jvtvKadeC6wcpiMkn2CKw/39uo1CMdErqxYe7vtnvwC3OaqO+UAiHxdt991M3uwYfgb7MtolKZOAocgiIvJZWTX9cWh4RYLEo2m8Y0zeBnPQOHhGC1rFHKDYvQc5ywAC0wOtVum8y7zfcV5XKVeDyGlDP7oBYbNgbFYap4atNErl8/6T9P5t2ms32bq/AUaClhzMZ8sVgGggygTuO9cD/8XXYN1GoTfSgrjRgcBCnQfX2IfG7zvxkGpQ98EG0Y4x8f+CghPA/nZcfh7733KIcEOY3CPBLLMslm08TjUcrlCoODwzW5fhmtdcMhIRKxGw4JiUQMrWlENhiGgdai1oZrhxgiZL4SFqAFQqfbbc2828rlMkrpGbd1dDIZXLA/xX6AUsF9xzCy3TaRd5u37wvwt9m2drJJvmnXbzdN/B12GLUS66ogQQiK//sJdCKJGBoKVm4AGsTQEPLZ1YGKzXVJfOLjZI45itgF50MlCJ1zXvkqip/5LNq2EaUSolAI/pRK4PtUjzuewoVfbZyuVKqglCKZnN7vTEpJKpVo2BUNDeVGBd6NFCQAjfZbKEgIaUZYgOY53Wy3TYbWUCyWGt96p4tavhx/512C1lGzg/sK5/gTRj3naUUlGAaV//fWYGNfTfBNW+vgdtMEy6L6ulPG3aWbggRv/wPIX/I91HbPC4rI8DBy7VpEPhiBUAMDqGXLUP39iHye2KXfJXnu+xttu+oZb2bozj9ROuc83AMOxH3BC6mcciq562+k8J1LID76OUw3smFkuy2XK5DLFcbJuMPIhpBWCOeA5jHtGCYd6d0222HSTCaJEJKhodzUd64R+elPSHzog2jbCuTSI9EakctBNMrwz36Ov/MuM08mVYrExz5C9AeX1/ZQxOj+pGGgo1G8F7yQ3A+vgAkEFV13SPA8rN/fQeQn1xG5+dfoaBQ1MDB+H6tSQRaKFD/3eaqvfV1LpwparCaDg8Pj/s2yTJLJOFLKaUV8N3NISKXitRC7QbLZTOiQMEeEc0Ahs2Yu223NaEWQUH31a6i84XSE6watp0olaD/VVgDYNsXPfh522621qAQpKX7xyxTOvwCdzW5e9QDattHpNO5BB5P/1ncmLD7QfUECpol79DEI1w2Kz+LFE4soolFAE7nm6slbjFMwkSBhqnbbZLQS2RAKEp7bhCugeUSr3m31qISRzgad8m6Lx2PEYhEGB4ennx6qFJEbbyDywx9g/vehml2OiXPooVT/31uxjzqSaDSC76tR3m0zxnWxbrsN+47bEaUSaostcF52HN5+L5pSJdd1h4Rqlez+LwLlo1OTf2sVxSIoxdDv/oBetKilU9WzewYHc0QiNvF4FKX0mJXwTNjskFAuV+nvzzA0lMMwjNAhYY7ptRVQWIDmCb3WbpsMIaCvL4PneeTzxZk9WGvk008jikXUwAD2ttvMrN3WYaLRCIlEjOHhfOcjG0olsgfuD+gJBRgNymWE6zF8622oJUtbPl1/f6axuptOu60ZWmts2yKTSbFp0zDxeLTWfhtiYKCvtr+YZ9GiLI7jMDxcIFDDhT5xnabXClDojd7jzMS7LZ1OTOrd1q2ohLogIZ1OUqlUZ7ZaEQK1fDmGIUkn41iW1YhKmPZqqoNUKlWiUbs7kQ2xGGrJEoynnmxagESlgh5YhOofaOk0QVss1pBjFwpFKpWJ97mMBx4g8oubEBs3olMpnKOPxTvwwHEzXNOJbKgLEjKZFKVSJYxseI4S/qZ7lGbJpPV2W6EQtNsmTiZNTNpu63QyqeO4NYeE+IwECUIELbx6u60Xk0kLhRJ9fWkiEbuzggQhqL7uFOJf/mLgcDDRrJTvIzyPymtObm7hMwmxWLTRbsvlCkSjNrFYbFwBEoODJN//Pqw/3onwFaDRQhC9/DL83fcg/81vo7bbbuwToFAo1eaz7EZiaqk0MsRusBZil6wJEjRBfLcRroKeI4QtuB6kk+02/9/3EbnmasyHHkJbFt6L96dy6mktOU43fw6Svr70tFtnM1a3zSHdimwQg4OkTz8N45FHUZn0aKdrx0EOD6O22YbhH18zyh17KiZTt0kpyWbTo1tw5TLpU18XGMJa9uacIa3BcxHVKv6ybRi+4WfjXkMTRTb4vk8uV2DRomwY2TAH9FoLLixAPUS7ohJKpWpttRTZ3G4bzhH/yIeIXnfdKCUYUqJtm9InPhWEnbWR6QgSDEOS7MF2WzO6KUiQzzxD8uz3Yj70YLASEgI0YEj8HXagcPHX8XfcaXrHqrXbIhG7JjwpjZvnGSlIUEoRufoqkh/5EDoam3gVpnwolaic9U5KH/2fCc4aChJ6ibAAhYyjG+q2xAc/QPTqq4JIAsva3LfXOkjMlJLCRRdTPfm1bX1ekwkSxrbbZqVumwO6Kkjwfaw//RH7N78J9l/6MjhHH4t7+OGbQ+umYGS7bSp1Wzabru0bFsi8/DiM//wHkilAB8F+dal87bWkAT0wwOC9f61JwzcTChJ6i7AAhYyiG+o247HH6Dvy8KBdNNFegdaISgW1dCsG7/3LtD/UpkMgjkiO2s+ZT+22ZvT1pdCazgsSZsFMh0lh8+8sN5Qjuf12gVVSJBLMZbkTFK5asN/gH/6I2mnnCf5ZkcmkkFI2BAmlUplq1W2siIQQZDIpNm4cqgkSNEJYSBluU7eTXitA4SDqHCElxONWV4ZJI9deG7TcJissQqBtO3Cn/v3v2/o8RwoSpvJum2/0cmRDq8OkEPzOGpENtZafyOU2F5+6m0T9D4DrEv3RDyc5Yt013WgIEsLIhhAIC1DXqXu3WZYkHo/U3IFlR73bjCce32xDMxmGAUJgPP5Y259zsVhqiBKaerfNM7rukDBNpuPdNhWFQhlpGugXvABcZ+rQOyGI3HjjaJfuxj+FDgkhExMWoC4yMirB9xX5/ObZnE5EJdTR05Ho1rXeI+4rhoeJ/PhK0qe8lsxRR5B+zauIfu9SxIYN037OkYhNOr152Z/L9Z60ejZ0M7JhKiaKSmjNyYBGZIN8+9sRvt8ItBtH7XYdjyMKeezf/nbC4wkhw8iGkHGEBagLTObdVv/mrFSQJmnbZke827z9D6DmfT/5RXoeSIm7/wEAGPffT+bE40l84uOYf/8bxjPPYP77X8S/9AX6jnsp5j33NH3OE7XbWols6HW6GtkwCbNptzWjVKqgTjwRvdVWwQ31Lylj/980g2FZIRHr1k56vDCyIWQsYQHqINONSigWyw21kNaQy7UvKgECw0+dTCIcZ2LTSq0RSuHu9yL8PfZArFlD6qy3IVetQmf60Nl+dCZT+28fYtNGUu9+J8Zjj074nCeLSmglsmE+0M3IhrG0o93WjKLjIU84YbMEe2ThAXQkgurvr+0VKYhPHs8eRjaEjCUsQB1ipsmk9W/S9dZaq+22idDpNIXz/w9MM5DQet7mDxLXRTgOKtNH8cvnB9f5k+uQzz6L7suOTxSVEp3tRwwPERmz6TxZMmmdkYKEhUa3BQntbLc1w3Fc3GOPDayBstngi0gigU6lUIsWBQ7jUgYu5paFc9jhUxwxFCSEbCYsQG1mNlEJhiFHjOdolFJti0pwTnolue9fjr/TzgjfR9TmOYQG96CDyf3sJvyddwGtiVx3LUhjcofo2vxH5Bc/h1JpRuq2ViIb5gPdEiR0qt3WjPyBh8D22yOrVXQ0ik6l0InE5lWR7yNcF+eIIyew5BlNKEgIGUk4B9Qm2j1MWlfHBdYlM1vxNEVrzHvvxXjsETAtvP32wx85u1Euk33RC4In0aSdQqWC8FycP/2JyM47z2iYtKXIhnlApx0SZjJM2m4Sjz9K9LRTYd06tGkGwpb6ELNS+DvtRO6qa9GLF0/ziKFDwlzQa3NAYQFqA50aJvU8D9u2GRrKtbWv3xTfJ/uCvRGOg042yaEplxFo+Pe/KSVTM5rnmVVkQ4/TCYeEVoZJO0F203q48ELEDTcELTdAZ/qovu51lN/xTnR//7SPFTokzA1hAVpATOTdVm8bzNq7rRaVkM1mGrYo3SL5nndj3/zriT9QRNAGEhs34u/3IoavuZZWFjETOSQsFNrlkDAd77ZuUv+d5Z9agf/Y42hp4O+ww6RJslMROiR0n14rQOEeUAs0U7elUu0dJi0USti2hW23zx5nKiqnvR4sE1EY8QEqQEgR5MaUSighKJ56WkvFB0JBwlR0Wt3WCnWHhPg22+Dtsy/+Xnu1XHwCQkHCc51wBTRD5iKZNJVKYJomQ0PDE6qo247WxC66kNh3vhXMDiUSCMsKPMEKBTRQPeVUip/53JRR1pMhn3mGyO9uI756FS5Q3WkXnCOPmlEbp5dpNbKhV9ptkzFhZMMsCCMbukuvrYDCAjRNutFum0xkIKUgm81QLlcplcotPwcxPIwoFVHpzNTfXLUmetPPiF/+feQjj6A9Dy0l/jbbUnnTm6i+/vTxEu3p4HlEL/kukRuuR+RzSMNAEEzeq0WLKb/r3TjHn9DS8+slZipI6LV2WzPGRjbMnlCQ0C3CAjTP6EZUwnTUbXUF1IwFCVpj/e52olf8COvee0AptG3jHH8ClTe9BX+PPSZ8zo2oBNej/Je/otavR6XT+Hs+v7XCUyN6yXeI/uBydDwRrHYMiWWaKNdFr1qNtkxKH/8E7pFHtXyOXmG6goS5VLe1ysjIhtkSChK6R1iA5hFz0W5rxozf9FoTu/ArQSvN8wLprGEgPA9cFx1PULjoq7jHvrTxkE5GJchnV5M6442gFXrRZrmukBLLNHBdF/HUCvyddiJ/2Q8mDkCbZzQTJPR6u60ZjciGXKEtBTMUJHSHXitAoQhhAiYaJjXNzkQlzISZChLsm39N7NvfDMwi+/ogHodIJJhkz2QQpSLJc89BPvVUV6ISrNtvR+SG0f0Do27XSqG0xjBN1BZbYDzxBObf/9bWc88VEwkS5mKYtN2MimxoC6Eg4blIWIBG0E11Wyu4rke1Grzpp+w6aE30su+B7wdT6xM8WZ3JIIoFUjde35WoBPnsswjNhMIF3/MRQiCTCfA95OrVbT//XDDWIaEX1W2tUiiUkVLU3ApmR+iQ8NwkXLvWGNluG+ndVm+3FQrlce22fL6E1ppkMjau3ZZOJ2oFK99Su20yisUS2WyGWCzWVJAgV67EvO8/6OjkHw5CSoRhYP7sRkof+1jnw+EMAz3Jj0BrjfIVhpRB8VsA7bc6dc+/bDZd2w+cX+22Br6Pee+9mA//F7TG32knykcfRSyVpFJxZi1IqEc2RCKRRmRDf3+mEdmQTMZrQpwgsqFScWqCBB8wan9C5hML513eIjNRt6XTiUnVbbnc5myf6arbWkGpwLQ0Ho9SrVYn/fYshodB+TBJu05KGXx/NAz0pk1dSSb199gj+GG7DljjZ2R85SNzBUQ6hbf7eHHEfKSubqtvlOfzxXkhMhiLec89xM//EsYTTwSvKwAh8bfbDvXJT5A84sg2CRKCyIZMJnDXqLffNmwYIhaLkk4nGBrKE40GRSoQJGiU8oPXdChImFc8Z1twvd5ua0a5XEEp1bT/rvtrTtbeaJcBIWrDpATFTHseasyeTKdwDnkJautlyGefnTgWwvNR69YhDzoIY9ddunJNnWRsu833/UYWznzCvOsukueejfHoI6h0GrVkafCnry9I233Xu7B/d3tbhqVbjWywbRlGNsxDnpMFaKZRCe1KJm0nUwkS1FZb473whYFZpNaNwhNs3gZO21r5oBTVk17Z0WttkEhQOuc8dCqNfOopRKEQ/AKUQgwOYjz1FP7y7XDPPnteOyRMFpXQ7ciGtuB5xM//IiKfRy1ZCiNbupFIcFuphPrkJ0m0LZBv+oKETCZFf3+G/v4spinCvaB5xnOqBTff2m3NGClIcN3AIUEMDmLf/Gvks6shEsE5/AjMv/4VWSwiUoEcWOlay05rRC5XM5M8pSvXDOAdfDDFL3yR6Pe/h/ngA4j164JB1FSa6ktfRuUdZyEWb0lfLbKhG63BdjF2mHSswGCkIMFx5sfGuXX3XRhPPRWskidqbwmB6h9APvMM8vbbiB919Kz3t8YKEjZsGGy03+oOCclkAt/3sSwTIQSbNg3jug7hbND84jlRgMYOk9bbbSOHSQuFYJg0lYqPGyYdWYzGDpPmciOHSbv7om8IEmwb/aUvEr38MkQ+33iiwjRhiy1g7Tr04BDaCrTlwvMCdVwmQ+Eb30QtW9bV6/ZeuB+FF7wQ48EHkatWBnsJu+6K2mab4A6+olyuEo/HqFadeRHZMHKYtNlsTLFYxrYt4vFYRyIb2o3x0EOBBVOkSX6TbYPSuP/4J7GXv7zjgoRq1SEej9byFD1M08Dz/EZ6aihImD8s+AI0X9RtraCUplQsE//0J9GXXBKklabTiJq9DdUqrF6N6uvDe8ELse69B+H76HSG6mtOpvL6N6Ce97w5uXaEwN9jjwmdGADK5XJtKDbe05ENMx0mrSffJhIxqtVq2yIbOoZWE698JsCtOhhKkUzGOyJIqFYdMpkUQohaXInP4GCORYuyoSBhnrJgC9BCarc1w/nzn4lffjnCtlGx2GZ1mwZl20EM99AQOpVm07/vR1TK6Fh8VnY63UDrYIWXTiepVKo9F9kwVbutGZVKlWg0KK6zjWzoNP72OwACHCdY6QAoH1EoInwPbRgQiQIaf4cdKBbLpNPJRodgNmwWJLj09QUuCVrr2nsw8IyrCxIymRSlUqXmkOCjtUSIBfvxtmBYcL+hhdpum4zoddeC6yL6+pC1b3xKaTQN4zqwLOzf3oLY+DH0llvO4dXOjJGRDUNDubm+nAbTbbc1o1Ao0deXJhKxqVadDlxle3APPQy19VbIVatRW2yB3LgBsWkQ4Y14zlqjFi3COeQlMMIhwXGGZ33+WCywtKq/Jz0vSAsuFMojJNr1PaIkGzcO1b4cemhthKugHmdBqeAiEQPLEvNa3TZTrH/+I2i9NYqP2lx8auhoFCoVzP8+NBeXOCsCNVQgSJhrJlO3tcJYh4SeJRqlfPY5YNsYjz2GXLs2WPmYVvCndjdRqZD44ufBddvikGCaBn19KRKJONWqQ6VSIRKxQ4eEBcaCKEB177ZkMlDKJBJRolFrzr3bOkndu82UEnTgpwY0/zCbh29Gf4QgQcq5+fl3yrutWAycLOLx2KyP1Umc446n8prXINzaSk1phOcGf0wTf9tt8bfaGvv227B/+QuUUpTLFWKxaGPmbLoIIUgm4/T1pRsmrsVi8MVRa90QJEQidsMhIZGIoTUNhwTDMNBa1OaC5qfN0XOFeV2Axg6T5nIlKhUH0zRrL36B47h4nuq5YdJWEQISiVjDu6265/PRSqG1RtfmfcY9ploF28bfZX4Od5bLZZTSczIb1EnvtrogIRq1Mds2Q9MZjNWrUQOL8JcvR22xGLV4Mf422+Lttjt6YFGQL6U1kRtvgNrzUjVBwnSJRiNksxls2yKfL46KsagLEuqDvPX2W6lUaayICoUSWlPrbgSdEKX8cBXUw8zbPaCJ1G31yIN6VIKUkkjEIhIJbqtvZve6um0yJopKMF9zMtb1P4VKBR2LNQZOGzJYpaBaxTn+BNTSreb2CbTIXAgSuhWVMC8ECY6D+e9/oVNJdF+WyT7OdTKF8dijiMFBdH//tAUJpmk0Vi6VXAH35z/H+v3vsSpl1JIlVF/5Kvzddh/lkFCPbKg7JISChPnJvPutzETdZtvB06tWHYQQNRsPu7FcV0r3rLptJIYhSSbjWJZFtepQLJYaszHefi+ietIriVz/0yBsLhZH1jZtdbWKKJXQA4son3PuHD+L2dEtQcJs1G2t0vOCBM8L3mxiioaJlOB7DfsnZwpBghCB9VU0GgnEQXf8nti73klk5crgDkqBlES//z2cY15K4fzzKRgG2Wym4ZCQTMYplaqhIGGeMm9acLPxbvM81WhxuK6H1op4PEoyGUNrNW/abRNGJQhB8YtfpvKmtwQ/nFwOPTQUxG9XKvg77UTuh1fg77Tz3D2RNtFpQcJcRSX0vCAhFkMt3QpRaj44K0pFdH8/Optt3DaZIGFsu63wl78SP+ONGCtXouNxdDqN7utDp1JoKbFv/hWp97wbv1oNIxsWEPNiBdTuYVLLMlFK4fs+hmHUPtB7640/o2RSy6L0qU9TefvbsX/+c4w1zxLNpHEOOoTCfi/q+Zmf6eJ3yCGhF5JJe9ohQQiqr3o18S9/EVwXrAn8Bz0PHIfqSa8a9e8jBQmVioOUYnO7reLUvvhpkl+7GDE0hEqlRg++CgHRKFpKrDv/gH3nnZSOOiqMbFgg9HQk90zabbGYPekwabAfJIjFIqPabQDZbKZhFNkLNGu3zYT6rMrQUG7eBp5NhBDQ15fB87xZOySMbbcVi6U5/VlFoxESidiozfdeQeRyJN/xNswH7kcNLILYCOVepYJcvz6IUv/upejFi8c9PptNAwLDkLiuR7FYajxHsWYN2cMOqbWQJ1cEilwO55hjyV/yPWzbIpNJsWnTMPF44Iq9YcMQAwN9NRFRnkWLsjiOU3NIUIQ+cWEk97ToVlRCsLldJhqNzLkKaVrtthkwnciG+UhdkFCX4bZKLyaTVipVfN/vyd+ZTqcpXngR3gtfhMznkCufQa5aGfx3aAjv+c+ncOFXJyw+0WgEISSGEawwxxZY44knwHHQ9hQu4VJiPvRgy5EN4IeRDT1Gz7Xguu3dVq06RKMRksk4Q0Nzo0KaUbttBhQKJTKZVFtsUXqJ2QgSeqHd1oxeFiSopVuR//5lmH/5C/bvf4fYtAnd14d76GG4Bxw4LsV2lLqt4mAYEtu2G/NPDYza9+DpNGMa7eQgsiEUJMxveqYAzaV3W7EYfFBHoxEqle7Z/7er3TYZE0U2LBTqH9TTjWyYC3VbK/R8ZIOUeAccgHfAAZPeZay6rb7ikVI2nCRGFn5/t93RyVQgYpgsir2WG+Xuf0DjHFNFNtQFCQMDfcTj0VrhCwQJz/VWXK8w5y24XkgmDd70gcV7N16U7W63NaNYLNX2v3p72n6mzMQhoRfbbc2YLw4JE9FsmHQyhwSdTlN91auD2Ad/4haZqFTANKmeetrm22qRDaFDwvxlTgtQLyWTlkrBmz6R6OybPhKxa22DCKVSmaGhXEcHK5UKptJjsQiGMeffN9rKVA4J7fRu6ybzySGhzmbvtkChODiYm7CFOJlDQvm970Ntvz2iWIRKZfMHgu8HGVeeR+WMN+Ht+4JRjwsdEuY3c/KJVPduq+/1QPACnkvvNq11RwUJde+2VCqB63oMDg53Le3zuSZI6JR3WzfpZUHCSCb2bis1/XCvS85HxsnrxYsZvupanKOPQWiNyOcR+QKiWET39VH60IcpffwT47KJQkHC/GZOZNgj27xCCOLxYA/HdT1KpUrttui4qISRUuuxUQnFYnFGK57JCAKvaJsgQYiglRKNRvB9RbFYmpNsG8syyWRSLccH9DKZTBIhJENDuVFRCcViaV4/12BVkSafL/acIAGCdlvQJgy+vM3kGoNuhcng4HiHBPnEE9h/vBPKZdTSpThHHQ3J5KTH0lpjGJJsNkM+X2wUxQ0bhkil4jWJ9iDZbAYhRE2QoGq2VZHn1F5Qr8mw53wOyDAEmUygXvG8oKjU1W2lUhWt9bhiVPd8U0qN+ZCZ/QvJNA0ymRTFYnnWgoROqdtaJZVKYJomQ0MLS5BgGLL2DVzXvqD0nrqtVZLJOLZtMTiY65mW0Vh1W30fZibUBQnt+l1pHazwo9EoGzYM0t+fwff9hiCh/n4eGOgjny9SLJYRQvNcmw3qtQI05yo439ds2rR5oDDYs6hSrbpEoxaxWKSryaQjBQnVqtPSm77T6rZWKRYD2WosFmvseU2J6yKffhrhOqjFW6AHBjp7kTNESkk8vtnCpheHOGdDLzkkTKZua4WxDgkN81ytMf77EPLZZ9HRGN7eewdO21NeWyBICB0S5hdzXoDGUixuXsZXqz5KBcUoEjFJJmO4bueTSYMXskUiEZuRQ8LYdtvwcL6noqTrgoSguFabK8EqFSK/+Dn2r3+FXLUqMIaMx3EOPZTqq16N2mHH7l34JIxNJk0k4sRi0Vk7JPQSdUFCsLlfnbPiOrLd1q6WYKkUhMwlk0HBsG6/jdi3v4X5wAPguSAEqr+f6smvpfzOd09ZiOqChEwmRbk8ch5oqCHRHhrKE40GRSpwSNAoFUjEnyuroF5izltw00VKQX9/oiavztFp77ZAeZdgaCg3rTd9r7XbmpHNpmurysLEdyiXiX/+s9h33om2LFR/PxgGolBADg6ili6h9PFP4O2zb3cvvMZkw6TBjFiy5wp/O+jrSzU2+btJO9ptzaj/zsqXfo/Ix/8HqlV0IgG2Db5ClIrgebj7H0D+ku9NowgpMpkUUspGZEOpVKZadenvzzA0lEMIQSaTYuPGoVpkg0YICyl77vt42+m1Fty80eUqpRthc9Fo5+OZq1Wnlh3UXIU0l+q2VikUSuNUSCOJXnsN9h/+gL9kS9Q22wRv+mgUvWgR/g47INetJ37+lxGF7n4YTqVuG+mQsNAoFEpYlkkkMoVdTZtoRd3WCo7j4jzyCNHPfBpcD90/ANEYSAMsC53pQ6fSWH/5M7FLvjudK6+5phsNh4R4PIbvq8aKqFIJBtjT6WQQW6JBa69n9tieS8ybAgRBe04pVev5d+N89Rfy+ILXzWFSALl6FdFLLyH+uc8S+8r/Yf71Ly1HbI90SBj3cywWsW/+NToeg/gE3zalxN9mG+TKlVh//GNL52+F6Q6TdjqyYa7oZmRDs2HSTuD98AooFKA/O05mDQSrIWkQ+cm1UG6+dznWISGMbOht5tWaM5j7qJJKxUgk4h13sJ5MkNDVdlu1SvzznyN6/U+CAb3aGzT23e/g7bEHhf+7ELXDDjM+7GSCBPOhB5Fr1+Jv1SQ91bIAjfnXv+Acd/yMzz0TZurd1qnIhl6g04KETrfbJsO643cgBEJKtFJMFLmqEwnkxo2Y/7kP78X7Nz1eKEiYP8yrFRBApeLhul7XHKxHOiR0vd2mFMkPfYDoj68IrOr7+tDZbPDfaATzvn+TfvMbkfUEyRkdemKHBFGpgPLHGUuORZtmMLXeIWYzTDqVQ8J8pVMOCd1qt01KtYKupa3KyVZ3UoLSiOr03m+hQ8L8YN4VIIBCoVJzv+78B0zwpg8cErrVbqtj/emPQTssFkMnk8GbEIJVUCSCzmSQq1YT+863Wjr+RA4JKtuPjkSmbnW4LmrLJS2ddypm693WrsiGXqTdDgndbrdNhL/NclA+SgXDoRPqi6pVsKzmK/MRhA4J84N5WYA8b6QgobObspGITTweQ2uNUrrj3m2jzn3ddYE5YywGjoMYGkJs3IQYHAzacdIA28L+xS+C21pgrCDB3203/B13wli3bvIHFYto28Y99NCWzjkZ7fRuCwUJzZmud1s3cF796uB/am3ukUalAGiNKJdw99tvhvL/UJDQ68zLAgSdFySMbbflcgWkFF1R4NUx//NvkBKxfj1yzRpELocoFhD5PHLdOuTaNWjTQpSKGE883tI5xgkSpKT62tehbRu5Zs14oUO5jLF6Nd4L9htnDNkqnfJuCwUJ45nzdtsEOEcdjff8vRD5HKrWYhN1h3OlEEOD6GiU8jveOaPjhoKE3mfeFqC6IKH+ImoXk6nbXNframQDELz5CkVEqRxcWD00qR6cVK0iN21sWQ1XZ2xkg3vEkVTOeicYBsbjjyFXrUKueRbjiScw1qzB3f8ASh/56IhwsNbpZFRC/ZvudCIb2nA2pFyJYTyIYTyMEOM9ztpJK5ENvdBum5BIhPy3voO3zz6IQgHWr0cMDiI2bUQMbkInEhS/+GW8gw+e8aHDyIbeZt4Mok5GX18MyzKnPTDajKnUbUIIstk0juN2XIEHkH7Fy7HuuXtz0ZkI30enUgz+49/oTKblc9VdBYaGco0CIB9/HPv2WzH/8heE46C22QbnmGNxDzwIIrNbVXQrmVQI6OvL4HlexxwSDONRTPOvSPkMQgRtLK0zeN5ueN7+aJ3uyHmj0QiJRGzKQjJX6rYZ4zhYv7+DyI03EHl2NToWo/ySmvPG0unt/UyE1hrbtshkUmzaNEw8Hq0ZlA4xMNBXG27Ps2hRFsdxag4JioXoE9drg6jzvgCZpqCvL9F4EbXCTLzbZuqQMBsyRx6Oef9/Nq9+xqJ1UIAyfWy6/0GIRmd1vikdEtrA2GTSYrHU8XC4TjokGMaD2PYtgINSS4EYoBBiCCnX4vs74TgnonVn3vjNHBLGercVi6XeWPFMg/rvrF3u7aFDQkCvFaB524KrMxtBQivDpNN1SJg1vo9ct27zSkOpza22YKc0uM00QQrk2rWzPuVUDgmzZa6SSTslSBAij2X9AQClticoPgASrfvx/R1rq6N/tPW8I5lMkNCz7bZp4jgujuO08XcWChJ6kXlfgKA1QcJskkmbOSS0jdqqRydT6FRqc2ysUpuLUTSKyvaDYU7eopsBTR0SZkEvJJN2QpAg5RNIuR6lJmsPWSg1gGHcD3RmVTlWkNBL6rbZUiiUkVLUxAGzIxQk9CYLogDNRJDQjmHSkQ4JHesPS4m3zz7gOuj+AdTWW6Oz/eh0Bt2XRS1ZitpyCcJ18JcuQS1d2pbTjhUkzIZeSibthCBBytVobdHsbaT1AEIMI+XGtpxzIgJBQtA+6iV122wZGdkwTprdAqEgofdYEAUIpnZIaLd320iHhE5RPeXU4MIrFTBMdDrdcEIgEgHPA62pvu6UKZ0LpstkDgkzZa7abc0oldrtkDCdBN7gW/SE/jJtot5+MwxJsVied+22ZpRKwbB0u1reoUNCb7FwdtcIHBL6+hIkk/FRgoROeLdpHcQQ13vHnXjDOy99Gc4xx2L/9jfguoFNvZRBC65cRlTKePvuS/WVr8K+8Xqsf/wTXAe17bZUjzsB9bzntXTecjmwe6m3JGZCt9RtrVB3SEink1Qq1VkLErRehBBVguIycSEK5NiJjijhxqrbLMvEti3K5d75mbeDYrFMOp3Etq1Zt27HOiTUBQl1h4T+/kzDISGTSVEqVWqCBB+tJUIsqI/MOWfeq+DGkkzaxGIR8vkCt956G+l0kqOPPrpjyaSZTAohaFmBNyWVCvHzv0Tk+p8G3mtCglboSATnmGNxjjyK+FcvQq5bGxQmgm9sOh6n+qpXU/rAhwI34RliWSaZTGraKqS5ULe1SiaTRAhZy5UaiYMQlVpbbeqVrRAbiEavRGsbrRdNcA+NlA/jefvhuu0zbZ1M3Rbs/6TbFhjXS6TTCQzDZHBw9vNVWmsMQ5LNZsjni43h3A0bhkil4jWJ9iDZbAYhBBs3DqF1YBMkZWRey7J7TQW34AqQELBhwyo++9nP8re//Y3nP//5fP/7l3fMPsc0DTKZVCNzvlOIDRuwb7sVuXEDKpXCPeIo5MqVJN//PkSpiFq0eHMbTuvANaFUonrqqZQ+/omWzplKJTBNk6Gh4aazriOTSYvFUtcFBjPFMCR9fenGaliIjRjGIxjGfxGiitYmSj0P398FpZY1PZZp/gnb/j1KZdF6gM1dbQfDWIFSA1Srr0Tr9vjmjUwmLRbL4wpNMhnHti0GB3O93zJSCvPvf8O+6aZg3ADw9tkH57jjkevXYzzyCAD+LrvgHXMs2aVbtG1VrXXggRiNRtmwYZD+/gy+75PLFVi0KNt4Pw8M9JHPFykWywihme+zQWEB6iCFQoHLLvsu119/Hb7vc+yxx3LWWe9myy237Oh5E4k4kUiX3/RakzrjdMx//D0Y0pvgDSFywwjfJ3fVNfg77zLjU0gpyGYzNev68eakvdxum4r6CmJ4+D8Yxm1IuQGlEgjhAw5SllCqH9d9Cb6/V5Mj+ZjmnzHNvyPEYEO+CyZKLcN1j5iyiE2H6Q6T1oelq1W3I5ENbcNxiH/2M0R++QtwqsEqXQOFPLJUAtNEx0eY5C5Zgv+JT2C/9rUMDuZQqh2ra00224fjOJTL1cY8kGEYjRVRPB4lFouwYcNQLbJBYxg2QszPyIZeK0ALpqH5yCP/5YMfPJuNGzeyzTbb8vGPf5zDDz+sKwOjQfaIRSIR64pDAoDxn/9gPnA/qm+SEC9Ap9KIZ1dj//KXlM+deQGqCxKCPKTqZoeEMe22XhAYzJRSqYxtV0gk/kS1uhEQmOZjCFEANFpHkHIQ2x6mWu1DqW0nOZKB5x2E7++BYTyBEPnaCmoLlFoOzG6mamy7bSqBQT2yIZBhd2Zvsh3EvnoRkRuvD8YMBgaCPKDhIYxSKTDg9X1IJlEDA+C6gRfiOeegLIvkCS9vy7B0XZCQyaQol6sNQcKGDUPEYlHS6QRDQ3lisQjpdLDXaxgS1/XxPD1vV0G9xIJRwT377LP4vs873vEefvjDa9h77xd2NbKhWCx3LaMIwHh6RZDd00wuLQQI2bJRKYyPbOhFdVsraA3l8gNY1hpMM49hPAT4KLUYpZYAMaTMY5r3Y5p3jnm0i5RPYxgP1XzfBmvWO/viuofieQeh1I7Mtvi0Okza7siGdiPWrCFyw0+DmJH6jJtSyGefDfYxLQsMAzE8HBQiy0ItWhRkAX3yk9jKb8uw9FhBQqlUQUpJNpvC930iEZvFi7NIKRtxLPF4HNOUYWRDm1gwK6DDDjuCww47ovH3ukNCLBYhGrWpVDq7KVutOkSjkXEKvI5hmuj6cGqzb2JaoWcp0S4USmQyKfr7g03Z+dZumwylHsL3N2FZ66lWt2Tk20HrGFrHEOJpLOtOHOdlwACG8SiG8U8MYy2BDFujVBKldsDz9muL2q0d3m2FQom+vjSRiN1zggT71t8iCwX8EXlSIpdDuF6wj1m3nvI8RKGIzqRBCFRfH3L1atxf/ILEq1+D48xekGAYZm2A16S/P/BStCwL1/XwfR8hBENDucZw9oYNmwUJWhvhKmiWLJgV0ER0OrJh/Pm64JBQw9vz+ZBIIApNip3vgxD4s4hNqH/7AxpvxoVQfACEyOF5a5AyhWlO/DvTehFSDmOaT2IYD2FZtyLlIL6/DN/fEd/fEYhjGP/Csm5DiNa/fLQzKmE2kQ2dRq5bh2a0v6GoVgIle/1ahQjact4I8ZAVrHrcf/9n1g4J9dZmNptGCEG1lkW0fv0gvq/QWrNp03CjOA0P5zGM0CGh3SzoAtSpyIbJ6IpDQg21bBnOoYch8/mg0IxFa+TGjehMhurxJ7R0jpHttrqTdGSWLtjtRoi1mOafsaw/YJr3IOUzTH9qXQE5PC+Q3k70KwvmP0yEWIlp/hmtozVRQV3aLtA6g1LbYxhPYhgPtvQ8OuHd1kpkQ1eIRQE9dYyI1ugJXCu01rNySAhsuNIjbLjy5PMFlNKjElNDh4TOs6ALEEztkNBuuuGQUKf8/nPxl2+HXPMsIp/b/IauVoIwOdOgdN4H0YsXz+i4E3m3VatOWxwS2kcZy/o10eiV2PbNWNad2PZviER+jG1fjxBDUx4hEAnk8LwNKFXGssbvKwSigsVIuQkpB9F6i0mOZqJUP4bxX2D6QpROerfVBQnRqN21vcnp4L54/2A1U9msrNSR2mqm/hpWKph5i4344ugG8n5/l11ackgwjGBkYiIbrtAhYW7ohU+SjlMoVBakIEEtW0b+u5fiHn0MwveRzz6L8exq5PAw/g47UPzCl3Be/ZppH28q77axgoS5w8O2b8Wy/oLWKXx/N3x/Z3x/N5TaAtN8ANv+JZMbgLpY1m2Y5s0Yxkos6w60vh3TvA/DqO8raKRcBTho7SLlKrQWNLPe0TqLEDmkHJroXxEiVyuM1a4lk/aiIMF7wQvxdt8DOTjYWL3rdBptWoG9lFJBzEgsiq63s7VGDg2hli7FOepoIFjhTce9vd5u6+tLIYSY0IZrrCAhlwsMSkeuiOoOCbFYkCcUvB78UJAwCxbUHFAz6g4JhUKx44IE6IJDwhjk449j3vcvhOfjb70M78UvnpE/3HSHSWfqkNAJDOMRbPsnKLU1EzsWuBjGYzjOCXjei8b8m0Mkchm2/Xu0NoA8hrESITyEUEAS190WKUsEqrg4EEXrOEJ4eN4LaisnQVBIFFrbBAIGD8N4hmr11bVrA1AYxuNI+SCG8SzgYpomtr0bsDfF4pYdFwn0okOCfPxxUu8+C+Ppp1HxBDqZROSGMVavDgqQbeNvvQxsK5BhDw2hIxGKX74A52XHNY4zlUPCTGy4ngsOCb02B/ScKUBCQH9/AoDBweaT/e2gWw4Js6WVYdLpOiR0Ctu+CdO8vyYAmBgpV6JUlmr1DEaq2yzrF0QiV6L1VmjdhxAFpHwUKfOAh2kOoZTGdbdD66U1G/+d0dqq7QEl0XqL2pzQJoI9ABulBmo2PhLHeRlK7YDWGUzzLizrr2itkdInEtmAlOvw/Tyel8FxXornvaRJpEN76EWHBPnMM0Qv/z72LTcHNlN1HBfhe6NECmrpUkof/uio4gPUZNPpca/d+jCpZZkzsuFa6A4JYQGaQ6JRk1QqRqVS7crA6Jw4JEyT2Xi3TeWQ0Fk00eh3CVpjkztcBO2uPJXK/0PrelR5mXj8U0j5LErtMOq+QZx2DsMYRMoNuO4O+P6uKLUdvr8TUMGy7kKINRjGRjxvKyBVe/wGpNyAEGU8b0+U2h2lUmhtYhir0Xopkcg6LOsJfF/iunGUAilXIYTEdffHcY4fdU3tppcdEsTGjRhPPQmAv/0O6Hgc+3e3Yzz8cHDbLrvgHHnUpDHwgVtBtPE+i8ejRKMRfF9RLJZasOFauA4JvVaA5mQOaOXKZ7j66it44IH7efLJx9l22+VcccV1Uz5Oa82VV/6QG2/8CUNDQ+y00868973nsueez5/WeSsVj2g0ECR0ysF6JHPhkDAdRrbbWmmlTeaQ0B0EIBFCT7H6qjtUb/5GahhP1DJ8RmcnaR1F6wRCDKN1EaUUhjGI50VRqp/gbZJE6z4M419ACcMAreO1/Z48wSooVduT2hYhStj27TWJdxZ4HMdJ4nmbjWGV2hYp1yHlWmz791Srizvimh08x951SNADA3gDA6Nuc447Ho6bnoFrqVQhErFJpxNIKWftej9dh4RoNEIqFWd4OHDPUMpvnD9kesyJCOHJJx/nnnvuYtmyZWy33fQjA6688odcdtl3OeWU13P++RcxMLCIc899D6tWrZz2MRaqIGE6tDOZdC4FCb6/PTDU9D5CbEKppWidHHGrA6ia23UdF8NYgZSrgfrPwsQwfEzzKSzrrpoYQaG1V7PjdxBiCCHKtbkfk3qRMoyNSLkRKU0sK45llfH9f+M4alTxCZAEBdJCiPVI+WSrP5Jp0YuChHYQyKLBNE18328pZHIkoSChe8xJATr44EO54YZf8bnPnc/OO+86rcdUq1WuvPJyTj31dE455Q3st9+L+fSnv0A6nebqq6+c9rnrDgmmaRKNzjymYKZUqw6u63Wl4E1Gp5JJC4XStFRI7cb3dwHiCLFpknsUa4KBPQle4i5SPoWUK2p7PisJihFIuR4h1iBEBSHqq1SN79sYhouUKzGMe2qO2U+gVASltkOpZWidRussSm1fa58JYCORyHoSCQdwcRwPrVegVHKiCyUoXC5axzCMzhYgCH5nlmU2QuzmMyPVbRBEyktptClyRdRi3A2iUZtCoUQ8Hmsk69ZzwBzHJZ1ONkxotfZ6rt3ey8xJC66V4bH777+PYrHIkUce3bjNsiwOO+wI/vCHO2Z0rGLRIRKxiMdjtQnoGV/ODM8XWNnUW3/dZLbttma4rke16pBIxHHd7gkSApfpA7GsPyBECaW2IBgM9WsriSFcdz98fxekXI1p3oVhrKZedILYhUJNOLAOIcoEhcqo/X8ZGETKAuAhxAqEyCPlBrROI2UBpdJAHqXqCjiJlHEM4yngrzjOM8CTBO3CPEK4aD3R5L4LRGvH6PxrY6RDguPM30n+idRtdUFCPB6d9ResQHziUy5XSCYTbNgw2Gi/1QUJ9cDGgYE+4vFobfA3cEiYD4KEXmDeeMGtWPEUANtuu92o25cvfx5r115NtVohEpmeNUfdISGVipFIxDu+PzPSIaFu+dFpuhWVUCyWyGYzxGKxLgoSBJ53EFonMc1/IOXTtRgFgdYRfH8ZwazQ9RjG42ht4fs7EBQpgWX9FtiEaQ4jRKFmp+fUZnQKBMq2PEoZtS9LPnBfTaZtECieijXnbAhWXANIWUapITwvje/3IWU/hvFMrQCtROtdGN10cAGJUlvW9p/6u/LTq8/PxOOxnhMkTEUzdZtSquGQUKk4s45sEELW9nGDvZ56YmrdISGZjNeEOIFDQqXi1AQJPvXXSUhz5k0Byudz2LY9zgomlUqhtSafz0+7AMHCFSR0Oyph7gQJEt/fB9/fAylXIkQBw3gQKVfVFGl5TPMRpFyF7y8H0ii1Nb6/M1DANP8KrAM2AHat/eZSX83ULVaUCny/pMwBdTVV0HqrP84whvD9Dfh+hOBDJw1E0HprtN5AoJBaC/SjVF255yHlepRahlJxDGO4qay8nfSyIGEyhBCj1G3Dw/kJ1W11QUIyOfM4+YkIBQmd5TnhhDAZC02QMFdRCXPrkGCh1PNqBWgFWvfV5nYGAFFb+XiY5n0IsbZ22wtwnFfieQcihETKMmDW9nT6CFpiMYI9nQqGUaUuYAgKTBBYJ2UaIWK1b71DCLEeIaoIsQnDeBSooNTi2oqMWnTDhprybQNKbYXv74BpPo3n7YxS23TtpzafBAnjvdtyTaXV03VImA6hIKGzzJsClEqlcRyHanV0nzyfzyOEIJWaub59oQgS2qlua5W5EiQACDGEaf4LpfprKxMQogJU0Tpea225GMbjBEVEoPUSXPcYfH+b2t6VjRAKIeoDkfWVkMD3Re1xddUaSLkJrTehlA/UV0c+SkXROgZswDQfQOsUrnsCrnsUSllIuZ4gsG7b2n7SJlx3T1z3SLrdkOh1QUIz77ZmOI6L4zhtLK6hIKFTzJsW3PLl2wHw9NMr2GmnnRu3r1jxFFtuuWRG7beRzGdBQi8lk86VIAFAyqcQYhildp7gX4N5IKWySLkRIYZqBUkjxCCBpNojEADU93t07b/1b9kCKSVKJTCMICdGKY9g1SPRWiCERdBWKwCrCFZmiwjsekp43gEolcb39yQQS1Rrq7Udau7a3X8r9qogYbrttmYUCuVQkDAPmDcFaM899yKRSHDHHbc1CpDnedx55x0ccMDBLR93vgoSOqlua5XuCRI0QUtMEszQFAhaY5vf5IFlThwhirVZoLpKbggpBxHimZpH2zBB8JyqHReC1Y5T+7tVO3YVKdWI5Iv6PpGqWbF4BIKCdE2VF0UIF8PYCDxcK0ZxfP/5tYLTG/SaIGEm3m3NCAUJ84M5KUCVSoV77vkTAGvWPEuxWOSOO24DYJ99Xkg2m+Xss9/JmjXPcu21PwOCHJrTT38Ll19+CX19WXbYYUduvPEnDA8Pc9ppp8/yeuaPIKFb6rZW6LwgoYJhPIxp/gchNhKsbJYTiAdG/86C3J6tMIxH0DpoxQhRqrXF6qq3jSjVj5Qba/16r7GSCZCAWZstcWsrpSCQLEjDzNfuaxAUK40QPlIWay2YfmAYKR9DyiUotWMt7rt36BVBQqvebc0IBQm9z5wUoMHBTfzv/35k1G31v3/ta98hm90P3/dr3yA2c/rpbwI011xzJUNDg+y4485ceOHX2Xrr2X+jLBQq9PUluhKpXRck1HvH03nT91K7rRnlcpA/U29JtAshctj2rzCMh2rWOWnAxzT/WVsBFWtqt82tWKW2ra141hIUl6GaXc4AhnE/EEOp7RDiCWBDbd8oUL2BgWGY+L6ofXsWNbdkge8rgs8Sr3Hfejx34LJgIMQwgXtCP0I8jWFswHVfTS82HSqVauN3NjzcXc/HdrTbmlEslkmnk9i2NesuwVhBwqZNORYvzjYECf39mYYgIZNJUSpVcByXINRQ1lw0QkbynDIjnYpejWyYblRCr9D+yAaFbd+Iaf6zJlUevWkuxCCWdSdKbYXrvoSRrTghcpjm3zHNvwEeSj0PpTK1/Z9+lNoKKR+vRTJsJEiCDvZ3glVRvX2i2SxEsGu3Ddb+HqEu2w4ECANobQJltI4ghKJSOYlq9SOjrq2XmIvIhna126ZiqsiGmTDfIxt6zYx03qjgukGxGPSK4/EY3XidFIt1Zc3ELr+9oG5rhZGChHb8HAM3g4fx/W0ZW3yCTf4ySvUh5QoM426kfIqg9bUSy/ojhvEUWkdQajF1xwOtF9VacxKltkHrrRFiMVL2o9TWKBWj3rdXahFKbVHb2zFrw6llNq+WFMFeUaT2bdcjEDeUkXIIrW1Mcy2W9ZuaDVDvMVKQ0OkPyVbVba1SKJSRMlhpzZaxgoRyudpITM3ni7VORbD6N02jds7gdaJU7wg9eoVwTTiCXhEkzJd2WzPaKUiQcgXBB/5IPzWFYTyEYTxUc6QuIcQwQqwC4gTFoILvL8J1D0TKZwmKhECI1Ui5hkBivTOmuRWWtSu+n8Dz1uC622IYGaR8BqX6UGrLmoIuXxtMdZDSQako9ZVPXZQQ5P6sI3DHtoE4Wm+NUllM80GkfKYmyd5+Vj+TTtBpQUKn222TEQoSepewAI1hrgUJvahua4V2ChICl4KRb1qFZf0B07yPYAXiIkSp5rem0Nqotc8iQB9aZ9B6I4axkmBlNFz7bxnTXAdsgePsiO8ngR3xvBej1ApMM41SW9XUdP+oFbFYLaLBAiRaV2t7R4p6QmqwN1RBSonWGq0VSm2H1lmkfAbLuhPHWYzWvdUO6aQgoVvttskIBQm9SViAJmCuBAmWZfakuq1V2idIiLF5pQGG8R9M85+1FpqJEBvROlFrfUUQooSUZVx3L0DUBAcg5RPUs4QC77YCSgm0XoMQG5FyKZ53UM05u4Dn7Q2UEcLF93euiRnq6jYTy9qA6y4G1td85Oof2AZB8XMQYhDTvK/merAfSm2NYTyGlCtqM0G9RbsFCW1Xt5VK2LfcjPnQQwB4u++O89KXQXzqodNQkNB7hD+FCag7JMRiEaJRu6OCBCllwz1ACMnQUG7etduaUSgEg7ezedP7/nIsKwoUAQvT/A/B6iONlBtq99KAjdZZhNgABPsvvr8zhvEMQQtPIaWPYQzg++D7DkplCMxFn8IwVqH1E2g9QBBQFwyxCuGj1ACu+xJM8z9o3YdSZXxfI6WH1lXq0dybJdz15xpDa7Dt3+O6ZXz/BQRx3o/V3BCijN/XmlsKhRJ9fWkiEbtlQUIn2m32DdeT+OLnERs3BnHdgdYd9aUvUPqf/8V5xUlNHz/SIcFxZi9IqDskZLOZhkNCMhmnVBq5IqoPrSZrgoRgZiyQ8YeroLAATUI3HBJGttsKhRKJRAzLsvD97rYnOkk7HBKU2hrP2xnT/Ddax2o2OEmCFUeVYJPXq/m4SbSO1lY5w9QdDYKguD7AxfNyaG0iRDA/pLWB729X20uq4nn7sjnGG4JithKldsZ1+xBiA6a5FqXimGYVpWStnaYRIle7HhOI1gpMFhhCyjUIcQ9B4N1DmObTaB3B83bH93dD60Ut/YzbzWwdEjrRbrNvuJ7kRz8MnodOpcA06xeL3LCB5Ic+QEEInBNf0fQ4oUNCbxGq4CahLkioq1rayUTqtkql2hAkLLQXZbFYQkpBLBZr8QgS1z0W398Nw3iCYDXjAhWC/B4frftG7KkEyiMhqkgpsCwwDAelErjuFig1QDCzA1pHaiKBZQSFw6hJqEcSQ6nnIeUGfH8PlNodpQbw/Z3wvG2RMhhADeTbBsGqJ12TZAcR4kHbJTApNYz/1lqGCcDFtu8gEvlpTWzRGwQflBCPT/931jF1W6lE4oufD4pPJrO5+ACYZnCb4xD/4ueh0ryojBQktJJLNpa6IEFr3RAkRCJ2Q5CQSAQr4LogIUhvFbU9yoXT6WiVsAA1oVLxcF2vbQ7WUyWT1tViiUSrH9S9SV2QEItFMIzWXnJap6lWX4XrHlab43ERQqBUCqWyNRPSeuG2CQLiIBqNA16t5QaB63UKrdMotT2+vzdKLWFzGqpBkC00lsAtW4gcjnMUnndI7bh2TbK9FUr111p6UeqzQVoH1wI+Ug6itYXWFkotrxWpLfH9XRFiGNu+pbaCmnvqgoRo1J7ytT8ymVQIwfBwnny+2KZkUrB/czNi40Z0MsmEun4h0KkUcs1a7NtunfJ4pVLg3t4uU+C6ICEWC2Te9fZbqVRpSLQLhRJaQyoVRwiBEKCU/5yXZYcFaAraFdkwnaiEbkQ2zBXtiWyI4bovwfdfhO9vjeftWItiEIwc8JSyipRJhNgO112B5/m1SGyHQBgwXLPqWUZdXSfEYM1hIVkrGuNRKo2UeSCN47wS1z0E398JpRZjGIuBBEFWkFlbmUFQjBRS1r3lBJCstRDriJojw7pajENvMJ3IhplGJbSC+dB/g8JjNtkxME2QAuO/D03rmGFkQ28QFqApmG1kw7SHSYtFRD5HtVLtSGRDL9COyAaltsHz9kHrZM1INFLb88kjhMY0PQxjA0otp1Q6GN838P0laJ1CyvUEGT1ZlHreiCLgIYRD4GC9jJF2PiMRQtX2sDRBkmoMKVcDOXz/caR0qa96gg8Yg0CUsIkgGiKF1ll8fysCGfdIJJDAMB5u+WfTCSaLbOjqMOl0O9KaiVdIExBGNvQGoQhhGrQiSJjWMKnrErn+p0Sv+BHGw/8NFD1Lt8J905uIvvOsSSMbxMaNGI8+Ckrh77ADesstx92nF2lPZIOB6x6FEMOY5t9rMzgRDKOAlIMopfG8RTjODghRwHFOxPNegJSPE43+CCE21BwR4gRO1gWEyNXaYWZTp2ohBlFqCab5Dwzj3wjh1RJWPSzrzxhGATDx/a1QaivqbbdgX2kA39+VoEAtYSLlm9Y2m/OIeoOxggSg68Ok3m67B30uz5t8FeS6gMbffY9pHzcUJMw9oRfcNIlGTVKpGJVKdUqHhGl5t5XLpN72/7D/eGfwDciqTem7LgjQu+wCP/85g7HkZoeEVSuJffvb2Df/ClEqgQYdi+EcfQzld74LtX3vTdePRUpBNpupTYq37pAgxFos689EIg9iWeuAMp7n4XkD+P7OKLUM398Rpbal/j3LMB7Gtq/CNP9LoI6zaquVPnx/G4RwUGpZbc5nLGWkXFVz2H4KpZaOGiQ1jPuIRP6AYTyN78dqRQ7Awve3RgivtoKK43kvmPAcUj6FUsupVl/X8s+lEwghyGbTeJ6PaRrdHyYtl+k74lDk+vWB4GDsB7XWiKEh1FZbMfS7P4A9/U5FPB4lFosyOJibtUNC7WLIZvtwHIdyuUp/f4ahoVxjHmrDhqHaOSNs2DBUc0jQGIaNEJ1vu/eaF1xYgGZAX18MyzIZGspNOCU+k6iExMc+QvTKK9GRSK34jMD3EeUy7P9iqr++mUKxjFyxgtSZb8Z45ml0NIquDd6JchlRKqOWLCF/6ffwd92trc+5E9QL9GxmnkzTqKmKNlGtrqNcrtYSUfumeGQVw7gPw3iIICtoAN/fDaW2xTAew7LuYrNvXD1DaCNSDuF5uza83LTeYtyRhRgiHr8XuB/fT6FUP76/K1pnMYz7MYx1uO6hKLX1BNflI+VjOM4r8P29WvqZdArDMGqGngaO41IotE9gMF3sX/6C5AfOBdcNxAj1lZDrIgoFsCMULv5aMJQ6Q7LZNL6v2uSQoLFti0wmxaZNw8Tj0ZpB6RADA334vs/QUJ5Fi7I4jlNzSAii3ruxCgoL0DzGNAV9fYnGi6jO2HZbsVhq+sEq1q0je8hBQdtgMmmy6yA8D/Gb3zC02x7EXn8a1p/vRQ0MgDHmm5JSyA0b8HfbjeEbbhr/7z1Iq2/6uuIqGo3guh7FYqmNljEaw/gvhvEfDGMtWvs1pV0/vr87Wkex7Zvx/e2ZbPvUNCXx+AOUSsvx/UD0AGbNC+4BII5S24x5lI+Uj6PUMqrVkwnEDHPP2GHSQLmlux7ZUMf+1S+Jf/5zyLVrglVQ7aNLLV1K6X8/2VLxAbBti3Q62TbrK60VmUwKKWXDIaFUKlOtuo0VkRCCTCbFxo1DNYcEjRAWUnZ2V6TXClC4BzQDxjokrFjxDK7rsPfez5+Rd5t9628RlTI63uSDxrTAcfB/+lOSr7cR//wHKpmcuLhIicpkMB59FPPee/EObj0htlu04pAQjUZqcym6Q7EBAt/fDd/fqWZWWiWQX0eQciOG8QBCrAW2JrAHGo/nKXw/gW3vx9DQ8wgKkEVgTro7lnVbbQ4ojdZWTS1XQqltcZyX0ivFZ6Jh0npkw2wcEmaDc8LLcY4+Bvv22zBqVjz+7rvjHHX0jNpu444bOiTMGWEBmiHFooPWPpdffhmXXHIJsViM3//+DzPaxJRDg2gpAzuRyRACrRTe2nXYd98NnoefyUx+f9uGXA7r3rvnRQGaiSBhc7vNoFJxGoN/naMuRqhgWffUCs8wUm7AMB5FiAJKLa9lE419C2mq1SqxWIRYLEW5vPl14fu7oNRAbRj1IYRwUWoJnrdH7VhJ5ppm3m2zdUhoC5EIzvEnwPEntPWwoSBhbggL0Az505/+yNe+9hVWrVrJkiVLOO+8D874BavSGYRSaKUmL0JaI6REpdN4uTymlCAkgdZ0AmovWFGePyamU0U2jG23DQ/nuxgZ7WBZt2Ka/0LrLVBqp1pRKiJEBcN4AKji+89nZDtOiBxKJalUFjecwEful2i9CM9bhOcdyOaAu7lnut5tnY5smCvCyIa5oTde/fOAYrHAhz98Dh/+8DmsXbuG//f/3sqvf/1rDj300BkfyznmWHQ0CtUmKiLPQwtB9WXHUxlYBFojdZM3Re0No5YunfH1zBXNHBKi0QjZbAbbtsjni10uPtQECf8gCK6LUZ/T0XoZoFAqg2E8Wcv+qeMg5Rp8f2dKpThK6SnmTHrj7TeTYdKZOCTMN0KHhO7TG++AecB//nMfd931R/bb78X88IfX8La3vZN4PN7Si1UvWUL1FSchfB+8CfY/fB9RreDvsSfegQfiHH0MOp1G5nKTLs1FoYCOxXGOO27G1zOXjHVICPYZUrVMGofBwVzX9xukXEkkchWG8W8s68/Y9u8xzb8ixNqatHtrpMwjRA4pH0GIPFKuxDCewvN2w3UPqHkJlhq+YL1Iq8Ok03FI6CZy5Ursn91I5Kc/wfzH32naz52C0CGhu4QquBmwdu0atthiy0YRSCZtYrEIhUJxxpENopAn9ZY3Y/3lz8H7xbJACITrBCHP2y4nd9U1qG0CxVTsaxcT//Y3IRbDj8U2z0JojSiVEKUSlTeeQenjn2jjM+4OlmWSyaRwHBfbtjqgbps+wazQz7GsPxLELqQBr2bBY+J5u6HUcoRYg2H8FyFKeN7+KLUY398d39+JkU4KmUyyEbPRK4xttxWLpRkPk9YFCZ0Rg0wP+cwzxD/9Kezf3YZwgmvQpom/y66UPvJR3COObOm4geTcZHBw9oIErTWJRIxYLIrreiiliERsBgeHicdjNYn2INlsBiFETZCgEEIgZaTte0G9poILC9AsEAL6+wPV0uBgC5P9lQrRq68iesWPkCueCgZLB/qpvP50Kme8CT0wsPm+vk/yS18gcu016GoVXVPDCc9DWxbOy0+k+JnPQSTSnifXRaLRSMOAtVAozdkHmhAbiUSuQogchvEkwaBqfMS/5xDCwXX3R+stEGId4FCtnlGLbxjfkjIMSV9fmlKpMkqQMFe0MyohmYxj2xaDg7mut4zkihVkTn4Vcu3a4L1Qf917HqJaRVt2MBc0RTzDhMeWkmw2PetgSNsOko6llPi+j1F/z9aKSv1n5nk+rusSiwVihEKhSLDX2/7ZoLAALTBm4pAwKUohNm0C3w+KzmR2I1qTfORhIjf8FO+ee0EpvD33pPqak/H2e9G0fbB6hZHqtmrVIRKxZ+2QMLvruQfbvgXf3wXT/BtSbkSp0Rk9wR7Pcnw/sPcJWm7NP+TqQorBweGuD3DWaXsyKZsdEqpVt+uChNQZp2P//g50LDZeyKM1olhCp5IM3v1ndF/fjI8/G4eEoM0Wb4wYFAollPIbDgnVqktfX5CSapoGlmWilGoUKADf93Ecl3y+0laHhF4rQL3ZnJ5HVCoe0ajX8G1rqW0kJXrRNMLIhKC4627YF1yAX3thz0cmU7f5vmoox+YiFTaYz0kBsrbPs5Ygd2jzfkBggroO399EIJud2nmiVCpj20HMdT7fXa+3TiST1qkLEoL9uhZf+y0gH38c6493ok1zYhWpEOh4DFEoELnxBipvOXPG5yiVKkQiNsnkzOLk64VLKTVqLrAuSMhkUpTLwTxQJGKNckjYuHGYRYv68Dy/9jiNUj5SygUryw5FCG2gXZEN02G+RzY0U7e1J7KhdYSooHVQbJTaslaE1tcMQoPVQjAomEfKp/G8ffD950153LkSJHQjKmEuBAnWn+9FeF7z4VMpQSnMe+5u+TwzESTYtkU2myYWq7ve50YNWE9HkGDbJvl8Edu2qFYd8vkSC12QEBagNjDbyIaZUq068y6yYbrqtnZENrSK1n0jcnxMPG/P2oCoi5RrkPJZpFwFGHjeobjuEUy3ieA4Lq7rduWDuqtRCUwe2dApAsNeMa2Ws2g26jAF04lskFKSTidJp5P4vmJwMNdk3yiMbBhLWIDaRLEYDK/F47GubMUUi/UXcm+LDoQQJJNx+vrSaA3Dw3mKxdKkb6iRDgnd7jp43u61eIf6KsHG9/fAdQ/C8/bF8/ZEqWVUKq/HdY8lSD2dPsGHj2zMhbSbTieTTsZIh4SZtIrEmjXYN15P5MorsG+5GYrTa0/6y5fXTzz5nbQGKfGfN/UKtRmFQhkpgzbmWOLxIGTSMGQjZLLZftFYh4RyudqYB8rnizVPyaDlZ5pG7ZyCukPCQixC4R5QmwjaLFVSqRiJRLzj+zPBm96p7Zk4PfnibNW7bSqHhE7h+zvi+9thGI/j+zuw+e2RQKkYUj6F7++G572oxeMH33QnckiYLe1Ut7XCTBwSxPAwsQvOJ3LLzYhCvuHwoRYvpvKGN1J569uaGuq6h7wEf9k2gTN8IjHxSshx0NKg+tpTZvW8JnJIGKlum6lSLnRIGM2CVMGtWPEUF110Pvfffx/xeIKXvex43va2d2GNjT0Yw8knn8iaNc+Ou/322+8iMk1581SRDe2krkJyekyQ0A7vtnZENrSCEOux7V9jGE+idYRgpsdFiBJKbYnjvAylWv9WLQT09WXwPK8tgoROqNtapS6nb+ZaIQp5ku94O9bf/4aOxdCpVLBf43mI4WGE51N5/Rsoffx/m7bY7BuvJ3nO+wNLq3h81FwcTuAkXz3plRS+/s22PLdsNo1SQSLuaHXbzF+bcxnZEKrgOkwul+N97zuLbbbZls9//gLWr1/HN75xEZVKhXPP/fCUjz/88KM49dTTR91mz8Bpt1Co0NeXIJmMj4ps6AR1QUK9dzwXg5sjaad3W7kc2L3UWxLdQuvFVKuvxTAexzQfAoaBGL6/K76/M1qnZ3l8KJVKpFJJKrX49VbopLqtVSqVauN3NllkQ+TKK7H+8XdUf/9oEYFpBiMI+TyRn1yLc/QxeAceOOm5nFe9hlIuT/xznwnCGZVq7AtpKam+/EQKF3ylbc/N8zwikQi+7886tmGsIKEe2VAXJPT3ZxoOCZlMINcOIht8tJYIsXA+thfOM6lx003XUyoV+cIXLiCdDtyjfd/nwgu/zBlnnMmiRYubPr6/v58993x+y+cfG9kwU4eEmVKtOkSjka4UvGZ0IiqhlciG9hDH959fMxptP9WqSzQaCBJacUiY63ZbMwqF0uSRDdUqket/EgyOTvKlTieTyLXriFz/06YFCKDypjfjHHsskWuvxbr3niC+ZJddqJx6Gv5ee7dlLm7sMCmINr0Ww8gGWIAihHvvvZv99ntxo/gAHHnkMSil+Mtf7u3KNTyXBAmd9G6bS0FCp2lFkNBtdVsrNBMkGM88HTgXJJvETmiNVj6R639C9sUvpO/A/Umcdw7mn++d0ONNLd2K8vvPIXfNdeRu+BnFL34Zf+99Zl18JlK3DQ8XJhUkzJRQkBCw4ArQihVPse222426LZVKMTCwiBUrnpry8b/97S0cccSBHHPMS/jAB97H448/NuNrqAsS6i+iTjNSkNCtb0YzVbe1SrFYQkpBbLLk2HnKSEGClM1/Z3OlbmuVINuG2op4BHXV2mSvUc9DPv00cmgIUSwhhoeRGzcQufEG0m98A/HPfabh+t5JJlO3jRQkyGZZXtOkLkjQWjcECfVZsUKhRCIRq7VsK409Va1FbS6o+4PanWDBteDy+RzJ5PiNtlQqRS7XvN1xyCGHsvvue7LllktYvXoVP/zhZbzrXf+Pyy77MVtvvWxG19EWh4QZEChrgnZBpwUJnU8m3Uw9smEuHRI6xXQcEnq53TYZkzkkqK23RieSiHIJPVbUozVy9WpEuaZ6jEY2r5RqhrvRH1yOWrKUytve3pHrno66rVWHhMmYyCEhaL8NNULshobyRKOBai4QJCwch4QFtwKaDe9//wc59tjj2HvvfTnuuJfzjW9cAsDVV1/Z0vEWmkPCXEUlzLVDQqeoCxImckiYD+22ZkzkkKBTaZzjT0BUKuCP+UJWLgdigrqQIDEimrz+dyGIfu9SKLdXmj+zYdIwsqGdLLgClEqlKRbHfzPJ5/Ok0zNTMC1atIi99tqHhx9+qKVrWSgOCd1qtzVjLh0SOkm1OtohYb6125oxkUNC5c1vQW21FXLdenA2f3mRw8NBe00piEQCk9Ex6EQCuWE99p1/aNs1znSYFKbnkDAzAkHC2972Nr761f+jUCjh+x6Dg0ML3iFhwRWg5cu3G7fXUygU2LhxA8uXb9f165nvgoS5Tiat81wQJKRSiY57t3WTiQQJapttyH/rO/g774wYGkKuWoVctQpRCL406lgMNTAw8T6RaQbtuHVrZ31tU3m3TUUzh4SZUhck+L7P9ddfz913383ZZ5/NmWeewZo16xgcHOQf//g7w8P5BSdIWHAF6IADDuJvf/sL+fxmSfIdd9yGlJIXv/iAGR1rw4b13Hffv9htt91bvp75KkjohWTSsSxUQQIIlNJEIva8bLc1YyJBgsr24xx+eJDh4zhBfo9pgmUFs0CTbfArFajkEk1UdFMw03bbZLRTkGBZJtlsHx/72McwDIOLLvoKL3zhC1mzZg0//vGPuOKKK3n/+9/H7bffRj5f5IorfsjTTz+9IAQJC64AnXTSa4jH43z0o+fxl7/cy69+9XO++c2LOemkV4+aATr77HdyyimvbPz91ltv4dOf/ji//e3N/OMff+OXv/wZ737325DSGDeYOlMqFQ/X9brmYF23r6mHvM2EXmi3TUZdkBCLRTCM+f/SHdlu01o3Wj/zsd02GXVBQjRqY5oG8oknSL/+FGLf+TaUK6hsPyrbH6x4HCdozU3y/EW5jE4kcA8+pKVraaXd1oxSKdibbLXlLaUglUqQyaQwDMm2227PySe/jieffBIQLF++nKuuuor99nshkUiEb37z6zz77LN84xvf4NOf/l9830cIUMrvifdnK8z/d/EY0uk0F1/8bQzD5KMfPY/vfOcbnHjiK3nve88ddb/6krfO0qVbs2HDer72ta9wzjnv5jvf+Qa77LIb3/3uZWy11dazvq62CRJcFzE4CE1cflsVJPRKu60ZC0WQMFFUwlxENnSDhiAhGiH5/vdhPP00KtuPzmYhFkPH46gtl4BpQbWCGB4afxDXhaqDc9zx6C23nNH5Z9tua0argoRYLEo2myESsXEcj8HBEsVilTe/+e309w/wve9dyhvfeCae5/Gd73yXt771baxbt46bbrqRU089lccff5ybb/4liUQC25bzVpCwIL3gepVk0iYWi1AoFGfskGA89CDRH/yAyC9ugqoDhoFzzLFUzjgDb/+JW4uZTAohmNIhoR3ebd3EskwymdSsLVHmgqm82zKZJELIlhwSehnTNOj7659Rp52GSiTAnmCPslLBWL0atEJn+tCxaLDnUy4H6b977U3+Bz+adsLpxMmk7W9XpdMJDMNkcHB4yvtalkkiEcc0DXxfUSxWqVZH7/Pdcsuv+NznPslb33oWjzzyMHfeeQf/8z+f5gc/uJQ1a9Zw2WU/4P3vfy/VapVbbrmFLbfcEq01Q0MlPK/58+s1L7gFtwLqZVoVJNi33EzmlScRueYqKFeCdoXnYv/y56RffyrRyy+b5HzNBQm93G5rxnwUJExX3dbpyIa5wvN8vF/+CqEUYqLiAxCN4m+1FCwLtEKUy4hKBbVoMeX3vI/8j66cdvFpd7utGdMRJIxtt5VKDoODxXHFB+ClLz2eT3/6i5xwwit43/vOJRqN8qc//Z6zz/4Avu/xta9dzDve8R5KpRKf+9wXKBarOI43kVFEz7Ow1vo9TiuRDcajj5B8/9lQKUMiOVodFAySEP/sZ/B32gn3kJeMemyzyIZuDpN2grmKbGiFmQyTdjKyYa7xNm7E0BohBXqy5xWNoRMJiueci3fQwWCY+DvuCNHpFeTZRCW0ykSRDSOpO7sHMz8ehULzgWohBEcddUzj75dd9mPi8QSLFi3i8MOP5N577+b887/KL37xM37721s48cRXse++L+zY8+sk4Qqoy8xUkBC58gpEuTS++EDw93gcfI/o97834ePHChJ6Ud3WCvNBkNDqMGmpVEYpPe/3ucai+vuBQEA86cq1ti+rl22Dv/c++HvuOa3i0y51W6uUShXK5TJ/+tMfcN2gLWxZJn19aRKJGEppcrkyw8PlGbt5bLvtchYtWgTAJz/5ea655kYikQjnnfdhIpEITzwxc7uwXiHcA5oDTFPQ15do5H5MitZk99kLMTwMIyfDx1KpgIDBP/8N3d+Pee+9RH5yLfZdfwLXRe+4E+Zb3kz1hJcT6c/iuh7FYqnnBAatkM2m8X3V1ciGqRgblVAslmY8zxOJWKRSyZ6IWWgX5j33kD7zTYhEAiJR/AlaYmJ4CJIphm7/HTo1vcHxeDxKLBZFKUWxWJ6zfcF77rmbD3zgXN7ylrdwzjnnEonYaK0pl11KpWpHWmSlUolYbPpJtL22BxQWoDliWoIE16V/910CWWqzb4GuC57L0G9vI3rddUR/9ENwHbBsEALhusHXzn32oXjJ96hk+zvynOaCXhMktNO7bcEJEpQi/fpTMf/+N2R/P75hjP5QLpeRxSLls86ifN4HpzzcXLTbmlGtVjjjjNNZu3YtP//5z1m2bNsp222dYOXKZ7j66it44IH7efLJx1m6dCuWLFnK/fffRzKZ5KSTTuL973//uJwzrTWXXnopV111FZs2bWKnnXZiyy235MEHH2RoaIhly5bxhje8gdNOO61t19qbvYvnANMSJJhmkPaoplip+D5IiX3zzUQvvwxtSHT/ADqTRvRlYPEidDoN//wn0XeeNd6Hax7TK4KETni3LThBgpQUvnox3m67owcHkRs3IooFRCGP3LAeWS5RffmJlN979hSHmdt220RYlsmWW27B//zP/+C6Lp/+9GcYGirNiXnuk08+zj333MWyZcvYZpvlPPvsajzP4/Ofv4DTTz+dH/zgB+yzzz4cfPDBnH/++Tg1S6RLL72Ur33ta7z5zW/mu9/9LqtWreJ3v/sdp59+Ot/+9rd5wQtewKc+9Sn23Xdf9tprL4477jh+/vOfz+pawxXQHBKNmqRSMSqV6qSChMT/fpzoj34QTH9P9AmrNaJYwHnJoRgrnkKuWYvOZhFSIIVAE2ySokG4DqJcJve9y3EPaW2YrxeRUpDNZiiXq10XJLSj3daMesLs4ODwghEkiHyOyI03kvjpdeinn0ZrjbfPvlRPORXnpS8DY/K90V5pt9WRUpBIxEe1297znndx111/5HOf+zKHH35U169JKdVwZzjzzNN59NGH+dWvbgMEb3rTKcTjcZ555hk++MEP8o1vfINUKsXQ0BDVapW99tqLK6+8kuHhYQ455BD6+vo47rjjeNe73sXrXvc6isUiS5Ys4WMf+xiPPvoo8XicQqHAF7/4RQ4//HC++93vzuhawxXQHDIdQULlDW9AR2NQLIwP5NIayiW0YeC96EXINWsgnUIagU27UhrlB8UHQFs2+D6J397c4WfWXeZKkDDRMGm792sWoiBBp9JUzngTxd/cinj0UfL/uo/8D6/AOf6ESYtPJ4dJW2WyYdL3ve88bNvma1+7EM/r/v7dSGug9evXEYvFSKcz3HTT9RSLRb7//e+jtSYSiSCEYM2aNbzpTW9Ca82jjz7Kl770pcZ177vvvtx5551ccMEFLFmyhBe+8IVks1kOPPBAzjjjDA477DC++c1vMjAw0Nq1tuUZh7TMVA4J/q67Ufi/r0AkgigWoFwCpxpY0hcLIASlD38Ulm2D0BphB9/ElK8mnufRGnPt2q5YAnWTbjokdDMqoVlkw3ynUnXwLYtEX2bS+/Rqu62Zum3rrZdx1lnvJRKJNNpbc0WhUMCygr2ee++9mwMPPJBtttmGxYsXc/PNN9fsfATPPvssAOeddx7XXHMNUkoOOeQQHn74YVavXs2vfvUr9thjD+666y7e8IY3NI5/wQUXcOSRR7LDDju0dH1hAZpjphPZ4Jz4CoZ/eiOVk18Lth2YMhoGzrEvJX/FVZjnnUtiy8DnTrnu5DMWBBuNKp3pSkZRt+l0ZMNcRSWMjWxYSEwU2VCnm8Ok02Emw6Sve91pXH31DcTjc/s78zy3sSJaseIptt9+ewAymQxPPvkkBx10EIsXL2b16tXYts0rXvEKlFLcddddfP3rX6evrw+tNb7vc9VVV7F06VLOPfdcDj74YM477zxuvfVWzjvvvJavb2F9pZqnFIsOkYhFPB6rDYyOv4+/114Uv3IRxc9/ETE8jE4miQ70k6oPk75gP+LpNKJQDAQHE+F5IATlQw8lXnNIqFQWhusyjBYkuO5wW2Wvc51MWiiU6Ovb3IJaKIyMbHCcIFqg19RtMPNh0l4kn8+NykTL5/Nsv/32PP3001Qqwc84nU6zePFiHn/8cT760Y+yfv36xv0Nw+CZZ57hHe94B7FYjIsuuogXvehFbLHFFi1fU7gC6gFmFNkQjWJsvRV9Wy8ZPUwaT1B95asDSfZEy36lELlh1NZbUz762LZENvQi7Y5s6JVk0pEOCVIutN/Z5mHp+dZu63VM05pw1Tg8PFxLYQ0Kkm3bOI5DtVolk8nw3//+l1tuuYXXve51jc+Iww8/nNe85jVcf/31xONxUqkU//rXvxrFqxXCAtQjTEeQMJV3W+m8D+AeeGAgbR3cBNVKkLeSzyMGN6EHBihc/HUYYV/TSmRDL9MuQUIvJpMuREECBG1hz/OIROx52W7rZZLJJK4bfCFNpdLk83ny+Tzr169vFJbh4WGWLFkCUIuCgFwuh2EYFItF+msOFgcccAC77bYb69at4+KLL+bMM8/EcRxWrFjR8vWFBaiHaCZImFZUQiJB/tLvU/rAh/C32QbhOIhKGR2xqZ56GsNXXYu3z75A65EN84HZChK6oW5rhYUoSKir2yzLqmUi6Z5Vt81Hs8/Fi7egXC6Tz+dZvnw7nnjiCW655ZaauCPNxo0bWb9+PQcccADJZJKbb76Z4eFhFi1ahO/7/PrXv+bggw9uHO+BBx4gEomw6667sssuuwCwadMmPM/D8zxyudyMlH/hHFCPMdIhoVSq4DhVlizZYuZRCZ6H8dST4LqorbZGZyZWGk03smG+0YpDwlRRCb3CQnBImCgqQUpBX196zsxxpxOVMB+oVCrcc8+fAPjJT67h/vvvY9ttl5PN9nP//fcRjUaJx+MMDQ2x/fbb88gjj7D33nvz+OOPUy6XcRyHM844g+uuu45qtcqHPvQhfvzjHzf2gJYuXcqqVasmPf+ll17KoYceOq1rDQtQjyEE9Pcn+Nvf/sZnPvMZVq5cye9//wdAdMS7zTSDPY5isbygBAkAqVQC0zQZGmouSOj0MGm7MQxJX1+6ZoA59xv0M6XZMGm9KA0O5roWCzLRMGmnvNu6wbPPrua1r33FpP++ZMkS0ul0Q3zw2te+lhUrVrBy5Ur22GMPbr31VgB23XVX+vr6eOKJJxgaGsJxHF70ohdx3HHH8eCDD3LjjTdywgkncPLJJ/OFL3yBaDTKueeeyy677ELfNGMzFsY6fgGxYcMGPv/5T/HrX/8SIQSnnHIqrut3TCzQLLJhvjOdyIa5Vre1QlcjGxwH6567kWvXoiMRvBe/GLV0q5YONR11Wz1hNB6PUSxOHVcyWxaCum0sS5duxZ/+9LcJ/y2XW8dnP/tZ/vnPfxKPxxFC8OSTT/LOd76TtWvX8qUvfYktttgCy7K46aabAHjTm97E6tWrOeecc/jWt77FF7/4RbbYYgvOPvts3v72tyOEIJ1OE4/H2X///Wd0rWEB6hG01lx//bVceum3KRaL7LnnnnzqU59im22WT7rykc88Q/TqH2P8+99AINWunPYG1LbbzujcpVKZSCT4cJhORtF8oS5IqH9Qj/xgmS/ttskolcrYtk0iESefL7b/BFoT+cl1RL9/KXLVKoRSoDU6kcQ5+mhKH/gQuhYRMBVj223Dw5MLDLQOfmeBwrPaMcf28e22yrxst82UHXbYgR/84AeNvz/++ON89rOf5d3vfjeJRIKTTz6Zc845Z5RRqVIK3/c5/vjjOf7449t6PWELrkd49NFHeMtbXk86neGss97DK1/5Svr7UxNHNmhN7ML/I37xV8FXm81KDQOkpPye91L64IebhK6MJxKxSaUSDA3lFkRMw0hGRjbMt3ZbMzoZ2RC95LvEvn4xKB2kkNYGoEUhjyiW8PbYg/yl30f3N3dWb9W7ra8v1VB6tpOF1m6bKWEcQ8iEKKW499672WOPPclk+gBIJiPEYva4yIbYN75G/ItfQAsRxBfXC43W4LoIrSl96COUz37/jK5hoQsSyuUKkYg9r9ptU9EJQYLxyMOkTz0FlEJns+Pv4LrIjRuovPlMSh/+yITHmO0waRCc2F5BwkJst82UXitAoQy7R5BSctBBhzSKDwTDqWMjG0QuR+zirwZ/se3RqxwhwLbRQOzrFyOGhmZ0DcViCaPmkLCQUEqjlCIWi87pMGkn6ERkg/2znyHKpWDlMxGWhY5EsH9+EyI3uvC1y7ttpEPCbPc/5/sw6UIm3APqYeoOCalUjEQiHnid3XQjolRCW038ziwLUS4TufEGKm85c9rnm1CQ4PvBh1Es3tQmvxcZ224TQuP7al7t9UxFJwQJ1l/+jDbMpi1cnUwhhoYwHnoQnUgSueF67BVPYsTj6IMPJv/yE6kmp5doOhmzFSSMbbeVSk7H221jw+C23XY5V1xx3ZSP01pz5ZU/5MYbf8LQ0BA77bQz733vuey55/M7d7E9QFiAepxKxSMa9Rq+bcZjjzX2eiZFSpAS4/GZZ8XXBQnJxx9FXXYZ9s2/BseFaITqy0+kesqp+LvuNotn1B0mUrfVWzBjBQnznbYLEjwXprL7EQK0InbxV7H+9jeE7wXfmLSGX/2K+Be/iP7M53BOemXLlzEbQcJctdvqYXC7774HWqtpuzlceeUPueyy73LWWe9hhx124oYbfsK5576Hyy//MVtvvazDVz13hC24ecAohwTTGp8LNAlNV0mTPUZrqj+6gsirX0Xk2muC2AeAQpHoFVeQPvV12L+5ZcbH7RbNvNu6GdnQTdrtkODvuDNiClGDKBURxSL2PXcjTQP6+lB9fahsFp1OIwoFkh/+INbtt83qWiqVKr7vT/t3NtfttoMPPpQbbvgVn/vc+ey8867Teky1WuXKKy/n1FNP55RT3sB++72YT3/6C6TTaa6++soOX/HcEhageUAQ2eBimibiJYcEcQzNYrV9H5TC2/+AGZ/L/Nc/sT/6UXS5gli8GJ3OoBMJdCaDGhhAFEskPvxBjP8+NItn1H6m693W6ciGuaKdkQ3Oq16FNg0oTdL2UgqRyyE8D2IxVCyGGvmlSMrAkb1aJf6V/5v2F6bJaBbZsPmUveHdJpt1Jibh/vvvo1gscuSRRzdusyyLww47gnvvvaudl9dzhAVonlAXJERecSJq662DN/8kgXPC81BbbYVz9DEzPk/kyisR5TIq2wciiPVuIASqvx9RKAarox5hJt5tIyMbFpgReNsECe6BB+Eedjgyl0MURifxCs/D2LAeISWYJioSmXh4WQh0PI7x6COY//j7rK5nKkHCfPduW7HiKQC23Xa7UbcvX/481q5dQ7U6/9wupktYgFpgxYqneP/738XRRx/CK17xUr71rYtx3annG7TWXHHFD3j1q0/gyCMP5h3veAv33/+faZ2zEdlg23iXXIKORBFVJ8j4qffePQ9RddCRCIWLLgZzhu2YYhH7tt+iIxE0Aq01QgpGveWFQFsWkV/+ovkqrAu0GpXQ7siGXqFtkQ1SUvjS+VSPOz6QXK9dg1zzLMbaNRhDg+hly/B23CkYA2hWxW0bXBfjqadav5YaxWKZUqnEv//9r0bBm+t2W7vI53PYtk0kMlp9mkql0FqTzy+ssYiRhAVohuRyOd73vrPwPI/Pf/4C3v72d/Hzn9/I179+4ZSPrW80nnLK6zn//IsYGFjEuee+h1WrVk7r3PXIhsjRR1P8yU/xnv98hFKIajVwvlYKb489yF11Le4hL5nxc5P5HML10LXCVU9WFWM/zCwTKhVEqQMT+NNgtlEJ7Yps6EXaFtmQTFL8yoXkfnw13jvfiXjVq9CnnUbpgv9j040/R22xxdStNa0B0dJe5PhDaW644We8853v4Je//HlPtNtCZk+ogpshN910PaVSkS984QLS6cBh2vd9Lrzwy5xxxpksWrR4wseN3WgE2HvvfTnttFdz9dVX8oEPTDzQN5ZCoUJfX4LYUUcwdMtvMf/1T4z/3Bdcxx574r3ghTNyQBiJSqbQpoHwPTQEf5RGSoEWevPnjedDNBpIs7tMu7zbyuUK0WigHMvlCm2+yrmjLkhIpZJUKtVZOSTYEZvEIQciDz143DCpe8CBWH/4fbAfOcm+h6hUIBYNXpNt4IADDiCVSvHtb3+Lk056BUKkF8QwaSqVboTBjVwF5fN5hBCkUr01PNpOFtbXvy5w7713s99+L24UH4AjjzwGpRR/+cu9kz6uXRuNIwUJ0VgEb98XUD3jzVTPeDPeC/drufgAkEziHnY4olJtfLtVOig8jc1VrRFOFee442be4psFnUgmDQUJEzOdYdLqq1+DTiQQ+fzEKyHfh2oV58ijUMtmLyO2LJPtt38eZ599NsPDw3z5yxfMy3bbRCxfvh0ATz89OthtxYqn2HLLJUQi7Rsy7jXCAjRDVqx4atxmYSqVYmBgUWMzcbLHQXs2GidySGgXlTecDpEIYnh4RBEK3uQSEEOD6HicyimntffEk9DJZNJQkDCeeDxKNpueMplUL15M6ZOfBtMMHDccZ/NeZKmEyOVQ22xL6WP/M6vnMVbddtJJr2HHHXfipptu5MEH75/VsXuFPffci0QiwR13bJase57HnXfewQEHHNzkkfOfsAAB1WqF17/+Nbz+9a8ZVQhyuWFOOumlnHXWmfi1Dfd8PkcyOX5JnEqlyOUm9+Nq50ZjQ5AgZdtnWrz9D6D4kY8iDAO5cSOikEeXyujhYcSmjWBHKH7qM/h77dXW805EN5JJQ0FCQD2ZNBaLUi5XGBzMTWkcWj35tRS+9g38nXZGVCqIoSHE4BACcF52HLmrrkHNYohyInVbtepzzjkfRmvNRRdd0PKxO0WlUuGOO27jjjtuY82aZykWi42/Dw4OAnD22e/klFNe2XhMJBLh9NPfwjXXXMl1113N3//+Vz71qY8xPDzMaaedPkfPpDuEe0BAJBLlf/7n07zrXf+PSy75Fu9977kAfOUrX6ZQKPCxj30So8dsaMY6JLTTwbr6xjPwd96Z6JVXYv3hDqTnoS0T8YpX4L3xDJzd9mjbuSaim1EJzSIb5jvTcUiYSVTCRDgvfRnOMcdi/uUvGE+vQFsW3gv3m3EkyEimikrYe+99eM1rXsef/3wPSqmWZm86xeDgJv73f0fv59b//rWvfYdsdj983298oa1z+ulvAjTXXHMlQ0OD7Ljjzlx44dcXtAsChAWowR577MnrX38GP/7xDzn00CPYtGkjt9/+W973vvPYdtvljfulUmmKxfGb1vl8nnR6cu+rTmw01gUJyWS87Q7W3v4HUNj/gMB+P59HpdJEBvpJpRKUOxTZMNa7rRMxAxPxXBUkjIxKmEl0+TikxDvgALwDZj74PPow0/due//7PwjQsaDGVmkWBlfnG9+4ZNxtQgje+Ma38MY3vqVTl9aThAVoBGee+XbuvvuPfO5zn6JcLrHPPi/gta89ddR9li/fbtxeT6FQYOPGDY3NxIkYudG40047N26fzUZjXZAQi9lEo/aoyIZ2oZMpdK3lWK06RKORjhS8uU4mLRRKZDKpxkpgoVCtukSjLgnPpXTzzYhcDmPxIiIveymy1m5rxa263czUu63XCk9Ia4QFaASWZfHRj36Ct771DGw7wsc+9slxL/QDDjiIH/3ocvL5fGPVcscdtyGl5MUvnvwb4MiNxnoBasdGY7FYJRIxicdjNQfrlg81zfMFH9T11t9s6ZVk0pGCBNcdnjdT9FNSLuNd8GWiP/0JqU2bABBSogcGKL/hjZTecVZX1Yxjea4mk4YEhAVoDH/+8z0AOE6VlSufYautth717yed9Bp++tNr+ehHz+OMM85k/fp1fPObF3PSSa8eNQN09tnvZM2aZ7n22p8BmzcaL7/8Evr6suyww47ceONPZr3ROFFkQyeZMLKhBeaq3daMYrFENpshFotRKpXn9FraguOQfP/7sH93exBcODAAhoHvOLBpkOhXL0Q89RTFL32561EbcxGVENJ7hAVoBI899ig/+MH3OP74E3nssUf40pc+y49+dC3JZLJxn3Q6zcUXf5uLLrqAj370POLxBCee+Ere/vZ3jTpWNzcaOylImIh6ZEMiEWup4M11u20yFpogIXLD9dh3/A6RTkM0aPFqrYOsn74+KJWI/PxnuEcfjfPSl3XtusJk0pA6YSR3Dc/zePvb30Q+n+eHP7ya1atX87a3ncExx7yMj33sk3N9eVNimoK+vgS+73cmUrtcxv7tbzCefhpt24gjjiBx0AEMzUCQ0CvttqnIZtP4vupdQYLvY957b1Bccjl0Xx/OMcfg7feixiCyFIK+V52EfOB+9MAASmmECFRvvlKNlYbcuAH3kJeQv/yHHb/s8e22athu6zK9FskdroBq/PCH3+fRRx/h4ou/TTyeYMcdd+LNb34rl176bY444igOPPCQub7EpnRMkKA10csvI3bxRchNm9BSIrSGL34e9eIXk/zGNxjaYmnTQ/Riu60ZvSxIkE8/TfLc92M++MBmI1ohiPz4Crx9X0Dh/y4ktv12xHLD8Nij+NFow9NPaxBaB0WotuLQkQjm3/8O5TLWn/5E5Gc3Itc8i06ncY59Kc6JJzZEKC1fc9huC5mEcAUEPPzwf3n729/Eq151ckPeCUEb7ayz3sL69eu54orret6TSQjo708AMDjYno302FcvIn5hkOmio9Fgr0BrcN0gEmKLLSjd9AvKk7QRe7XdNhWpVALTNBka6h1BgtiwgfQb34Dx+GOodBoikVoyqYZKBZkvIPbZG268kcr69UQOPhhlWjBiyLa+CtJaB6uiYhGUj1q2DebD/w0c0DVoNEII1KJF5L/9XbwX79/SNYfttt6i11ZAYQFaYESjJqlUjEqlOmtBgnziCfqOOjwwnJzIKUCpwHDy5S9n07cvGSVImC/ttsmQUpDNZiiXqz0jSIh98+vELr4Y1Z8NlGtaBxY4SiMtMygsmzZR+vgnKL/2dfQdcShi0yC6r2/UcaQUQXHxFWLjRqhWQQCRaCBWqOP7UC6hU2ly19+Av8v0Ej4hbLf1Kr1WgHpnhDikLdQjG6LRCKY5O2VT9OqrglVOdJIZJSnRhgG/+Q2JDeuAyb3b9PoNyMcfQ6xfP6tr6hY9F9ngukR++pMgqdQwEMPDyKefxnjmGYxVKxFPPYV69lm062JfczXYNtXXvDb4/Y0RwzRiNpQP1QrCcyEWH118IFjtJpLIfI7od78zrcvslWTSkPlBD7yzQtpNoVBBa00yOTufOPMvfw7+p9nQn2VBtUr0gftr3l2jvdv0H+4k+dYzyR74YvqOPYbsQQeQfsNpWLfdOqtr6wblcgWlVNv99lpBbNyI2LgRHYkg161DrluHcJzgd1P7/YhSCVEoYDzyMJTLVN/wBtSyrZGDm2BEYKIGdLmMHBwMZoJMc3IZthBowyDyq18FpqNNmO/JpCHdJyxAC5BRkQ1Ru/UDqWmo22offsr1SCRio6ISIlf9mPSbTse+7dZgD8ky0QLMu/5E6h1vI/b1r7V+bV2iZyIbDCOISC8GTtNCysZttY2d4I/WiOHhYJ9oyVLyl34ff6edkfkccsN65IYNyA0boFhE7b13sLo1p3hulgVOFbl69ST/vDCSSUO6T6iCW6C0wyHB331PrH//u6G0mgjheWBZqF12QWiN63oopTH+/S8Sn/wEeB46lRr1eB2NIopFYl+9CG+PPXCPPKrVp9lxesUhQQ8MwPOeh/hzsCrVE/0+6rdJSeSmn1F6/l74O+3M8E2/wPr9Hdi33YoYGkIPDOC87DjUYYfRt+/ewT5S05PXfv/W6I+LuVC3rVjxFBdddD73338f8XiCl73seN72tndhTZG6evLJJ7JmzbPjbr/99rvGOdSHdI+wAC1Q2uGQUD31VKLXXB18QE3wJhUAjoO//wEMLduWxAiHhOgVV4DrjCs+jetLJBC5HNEf/qCnCxBMzyFB5HPYN9+M8dhjYEi8PfbEOfqYyffPZoBtB0O/8oQT4O67J00grRcKHY1i/+Y3lD7+ifoBcI99Ke6xLx33EP+wwzB+9jM0QXz2hM+tWsVftgx/u+c1bpsLdVsul+N97zuLbbbZls9//gLWr1/HN75xEZVKhXPP/fCUjz/88KM49dTRriO2PYsOQcisCQvQAma2DgnePvtSPekkIjfegAawbRCBgkr4PrpSQcdiFD4UvPkbDgmxCNbNv97cIpoEbVlY99yNGBxEZ7OzeKadpalDgtZErvoxsa9dHOy11GXRCNSSLSl+/JO4xx7b0nnHRiUU9tuftGVt3s8ZWYiUCtqc8ThEo4jSxPELYym+7lTSP/8FolpFT2SIWztX9Q2ng2XNqXfbTTddT6lU5AtfuKCRSOz7Phde+GXOOOPMUVZYE9Hf38+eez6/G5caMk3CPaAFzqwECUJQ+L8LqZz2+uCv5XIwN1Isostl1MAi8pd+vzEjorWmWCwTRSNcN1DINcMwAin3DML45orJBAmRH19J4vOfRQwPobJZ1MAAatEiVCaNXLOG5AfOwbr9tkmOOjkTJZN6W22FyvShY7Gg0Pn+5j9CoNNp9KLF4HmoJc2Hg+u4Bx6E89a3IjwPUchvHm5VfpBsWq3gHnAg1f/31jlXt917793st9+LG8UH4Mgjj0EpxV/+cm/XriOkfYQroAXOrB0SbBvv4q8R+ciH4eqr8R57DM8wcQ86GOf4E8a1mKpVh2g6iZlKwuAgzbYDhOeBaaKzfTN+XgD8//buPCyqev8D+Puc2WeYQYRcSgWyvIqWgQaUqKlFlhhc06zMJS2ptFxyuWq37rVrLuWWmSWpaZp19ZotPhmoSC6ZktfcsszfFRU1l4BZmH3O74/DjAzMCjOcQT6v5+nxmcM5c77jk/Ph+z2f7+djt0Oyfx/EB34AY7bA0aYNzFkDwcXF1e39/KhZIYEpL4dy6RJwDge45s3dT5ZI4IiNBXv9OpTz56Gi9wMBVZ12LbexbK1WCVzLlrD26gVpQT4csXF8CrXDAY5l+X1arIgPRhwH8+ODA/tQDAPdjFkQJyaAXbYMzMWLroDmUKthemIouNffQExcc8E3k5aUnMWAAY+5HVOr1YiNjavVIsWT/Pzt+PrrrRCLxejaNRkvvvgK2re/I0yjJYGgANQE1DUhwW0zqTQBhpcnBLSZ1FBpRPSTT4J5/31+c6qnZTiOA2w2WB55FJzaeyM/r2M7ehRRUyZDdOZ3uB5fcBwU7yyAadRoGCdOCnmbgZoJCdJt37hmPh4xDBwaDUTnSiDZuwfWB/p4fe9AO5OaxjwHyd49YHQ6cM2auScj2O1gysv4QJydE/gHYxhonxkB8bBhuLTxM7RmReCiosD17g3Vra0jplWCTqdFlIeyQGq1Glqt1ue1GRm9kJTUBS1btsLFi6VYu3Y1XnppDFav3nDTdx2NZLQE1wQ4ExJYlg1oT4u3zaSBVjKw2ewwDxsOqNX88lrNiMdx/HGZDKZRz7qOiX49BUnhLogPHOB353shOvULNKOGQ3T6N3ByOTiNml9+UqvBmM1QvP8elHPeDGiswTIYKsGyDBQKBUSnf+ODX/WlRrudX8ZyBg+plP9sv/3m9T09Lbd5a4ttuzcVhnkLwCkVYMr+BFNeBkanBfPnn3wwbNMWug9W8llzQbDbHVizfj0GvrsU+xLjIXtiCKLbtblpNpNOnDgVmZmPoGvXZDzySJarK+nGjesFHlnTRjOgJiLQhIRQ1W4z3NYG0lWrwIweDeh0/ANzUdUSkcMByOXQz50HW7fukOTnQ7lsCcTHjt1Y/omLg2nYMzCOe7nWMp/y7QVVJWai3WdXLAsuKgpMZSXkG9bDPGQo7ElJdRq/N24JCSzrShxjKivBVFSAMRpvZKOpVHBER3t9L1/Lbb5YsgbC1uUuyP6zCdLt28HodXC0bAXzoEGwPJZTq/ROoNLT78cHH3yARYsWoW/fvhFXu02t1sBgqF2hXKfTQaMJbhYdFxeHu+++B7/++kuohkfqgGZATYivhASRSIToaDXUapXbZtK64jgOhoxeYPbsgeWFF/lnJCwLTqOBefhIVHzxJSx/fRyyT9ZBPXYMREeOgGNZcAoFOKkU7LVrUC5dAvWoEYDpxhcze/YsJHv3gJPLvGbYcQoFYLVB9u/P6zx+X5wJCeJ7ugIcX6WAvXSJT9BwLjlWzfJEpaWA1Qp75843PgPLQqOJgkYTBbvdgbIybdBtsR0JCTC+OhUVBTtR/sNBaLd+BfOIUXUOPhKJGCkpyRgxYgRKSkqwYsXKiNtMGh+fUOtZj16vx/Xr11wt70njQgGoCfFUIaG+y22+mM0WWOMTIJ43F2XF/8WfJ06h7L9HYXjzX7B3SgJ75neo3ngdjMMBKJX8chXL8okJSiU4qRTS/fugWPG+6z3Fp37xui/JhWEABhAfPVLvz+CNXl8J8ZAh4BRysGVl/EFnNYLqlQmqCrY6mvNLYsEstzWEmrXbRo8ei9jYWKxatRKXL18WbFyepKffj+Lig9BVy5osLNwBlmWRmpoe1Htdu3YVR48eQadOoZ0hk+BQAGpiDAYzHA4HbDYr3n13CZ5+eigqKspdtdtC3afHYKiESCSCXC7jkwKqzVrkn37Kp2s704prqkoikK9f53+3fgOzWm0wS2Vgb73V8wkcx/9XVWtN+Z9NiInRQKGQw2g0oaxMK3ivIU+12xhGgnHjJsFsNmP58iWCjq+m7OzHoVQqMWPGqzh48AC2bfsKy5cvRXb2ILc9QBMmvIihQ3NcrwsKtuOf/3wN+fnf4vDhYnzzzVaMG/c8WFZUa2MqaVj0DKiJsdsd+PzzTVi2bCmuX7+O9u3vgMlkgVgcnj49NpsdpmoVEqq3bJDmf8c/Q/G1WVUqBXv1GsTHj8GW0g22Tkn8TMls9twiAnB9+dvuSQ7xp3FXebYE0pISICqKXyasSoF2EYvBRUeDsVoh++pLWBYs8Jrd1pD8bSZ96KGHUVDwLS5cOCfgKGvTaDRYunQFFi9+GzNmvAqlUoWBA3MwduxLbufZ7XbYq1UAb936Nly7dhXvvrsQOp0OarUaKSn34rnncnHrrbc19Mcg1VA/oCbk9OnfsGjRfBw79jMUCgXGjx+P7Oy/gmHCOxFmGAYxMRp+N3+1kkAx3ZLBXLvqPZAAfGqxyQztZ5/D2oPvShv1/BhICwrARWs8lqVhDHwVgIovv4Y9jEss7JkziHkkE5BK4RCL+d5ItqovcgnfCI5hGH7jLsPiz2MnQp4aHtR4WRYqlcJVu81otHqt3eb8Ahf520xMGpVI6wdEM6AmQqvVIjf3WVgsZvTr9xAmTJiEDh1uh91uR3l5eH+5cFZIUKtVbhl49nbtIL5yxffFdjs4EQt76xtLXcap0yA5/BPfnkAsBmOxgKlWnoYTi2F6fmxYgw8Afh8Oy4Kx28HKZHBUBVKmqlwRwH922GzgYpoLGnyCrd1GgYc0BApATYRKpcKYMWPRsWMSunW7FwDqVyEhSGazBXK5DFFRSlfAMz8xFJLiQ+Dsds/9aDi+pI81LR2O2293HbZ3+Au0K1Yi+umhYCsqal3GcBz/XMlHFe9Q4GJjYcnoCdnOHWCqZjsMw/B7YgF+qY3jwNjtMA8aFLZx+CJk7TZC/KEkhCZCJBJh2LCRruAD3EhIUCoV4fyerna/agkJAMyPZcN+++1gzOZaXTvBcWCMRnASCYwTJtb6mXL5MjBVxVAhl/PtpBVKfkOqSATlksWQr/047J/J/OxocBIJOJ3OVUvawXE3go9WCy4qCqahT4V9LNWxLCt47TZC/KEA1IQFWyGhvqonJDAMA6hU0H7yKR+ETCbAYOALYBoMfPCRSmF4eyGsPXu5vY/45yOQFu7iO3nKZHw1BIUcnKwqjVsuBxwOKN5d4rOiQigwffoAc+fyM7jycnA6HbjKSjA6HZgKLTiVCvrFS91mcOHm7ExLnUlJpKMluCauvi0bguVq2aBSQK+vhKNdO5R/mw/Ztm8g+/wzsBcugFMqYenfH+Ynn4ajbdta7yH79+d89PTxTMXZulq6uxCWh/uH/HPwtdsUkEqlsDw7GsbEO6D87FOIt38LWCzgotQwZ+fAPGwY7B3+EvL7e0LLbaSxoQBEoNeb0KyZyu35TLh4TEhQKGAePATmwUMCeg/2/Hm+nI+vdUNnC+sLF0I08huUSjkUCjkcDge0Wj2/nyclBRUpKYhZ8T7sOj20ds7zc60wqJnd1hCdSQkJBQpApP4tG4LkKSEhGJwygOVCjgMcDnAh6EjqFEjtNr3Jgui4WEidgSnMhOhMSkio0DMgAkD4hIRgWHv15mc/vjZ0Wq3gJBJYM3rWY5Q8vnabKqDabdVbNoTz71EiEaNZMw1UKgUcDg5arTHiarcR4g9tRG1E9u79Hnl5K3D+fAlatGiF4cNH1WrQVdOlSxcxZEjtc5KSumDlyo/djsnlYqjVCphMZrcNo+GiUikhk0lQVqZFgP8b8vR6xKSngi0v42c4Nb/pHQ4wFgssmQ9Dt2pNvcZYfbnNYDAGNKthWQYxMdEwGs2orDTW6/613zvwzaSE1EQbUUmd/PzzEcyaNRVZWdmYMOFV/PTTIcyb9yaUSiX69HnQ7/W5ueOQnNzd9VrpYRlL6ISEgEVFQf/ecqifG82nYotENxISrFYwHAd723bQz5lb57HVtVUCUKNlgzl0S2K03EZuNhSAGom1az9CUlJnTJ06EwCQktIdpaUXsGrVhwEFoDZt2qJLl7v8nid4QkKArA/0gfbzTVC8swDS/ftd6dacQgFTdg4qp/0NXIsWQY/JLbvNR2dSf4xGE+RyKVQqJbTa2j1sgkHZbeRmRQGoEbBYLDh8uBgvvfSK2/EHH8zEjh3f4dKli2jd2ktV5iBFfEICx0Gydw9kW/4DpqwMXMtW0M1/my91I5XA1vUevvdQHXjMbqsHvb4S0dFqV5vtYAmR3VZSchaLFy/A8eNHoVSq0L//o3j++ZcgkUh8XsdxHNavX4svvtiE8vJy3HlnB7z88uSAfukhTRcFoEagtPQCbDYb2rVLcDseH58IgP/S8BeAFi6chzfemAmNJho9e/bGiy++DI3Gc7dOg8EMmUwMpVJRVcE6JB/DK4OB/6J2Lv15w54/D80zT0F88iS/CdVmA8RiyDesh+0vHaH99LM6BZ/6LLf5Uj0hwWqtCOrvUYjlNq1Wi1deeQFt27bDnDlv4+rVK3jvvcUwmUyYPHm6z2vXr1+L1as/xAsvjEf79ndiy5ZNmDx5PNas2YDbbmsT1nGTxosCUCOg02kBAFFR7g8Q1Wq+DbFWq/V6rUQiRU7OYKSlpSMqSo2TJ49j3brVOHXqJPLy1kHsYTOns0KCWq2ASqUMe0KCr5YNTkxZGaIfGwD20iX+tbPqdNWfot9PI3rgoyjfVQQuNjag+4Zquc0Xg6ESMTHRUCgUASUkCLnc9uWX/0FlpQFvvfW265cTu92ORYvmY8SI0W49d6ozm81Yv34NnnzyGQwdOgwA0LVrMp56ahA2blyPKVP+1iDjJ40PBSCBOFsJ+1PffiVxcXFuXwDJyd2QmNge06ZNRFFRIfr1e8jjdZGWkCD/aCXYixf57qkeMHY72Mt/QJH3ISr/NtPv/UK93OZNoAkJkbCZ9MCB/ejePdVtZty370N45525OHjwAB59dKDH644fPwqDwYC+fW88i5RIJOjduw+KigrDPm7SeFEAEkhh4Q7Mn/8vv+dt2LDZNdMxGNwfZjtnRhqNJqh733dfDygUCvz66y9eAxAQQQkJDgcUq1d7DT5OjMMO+Zo1qJw63WsVgnAtt/niLyEhUrLbSkrO1krrV6vViI2NQ0nJWZ/XAfC4RPzHHxthNpsgk4VuQzC5eVAAEsjAgTkYODAnoHMtFgvEYjFKSs4iLe0+13HnP/z4+ITQDxCRk5DAVFSAvXY1oPdg/7zO9wmqkQHXEMttvuj1lRCJGFy58gdatGgJIPKy23Q6ba1lXoAPQr6WeXU6LaRSKWQy903FarUaHMdBp9NRACIeUSWERkAqlSIlpTt2797pdnzXrgIkJCQGnQG3b98eGI1GdAqgYVtEVEjw0PXUpxqzH6WSrw4tEomg1eqh1TZ8W2yr1YaFCxdhyJDB+N///ketEggBBaBGY+TI53DixDG88848HD5cjFWrPkRBwXaMHp3rdl7v3mmYO3e26/WyZYuxfPlS7N69E8XFB/HJJ2swe/bf0bFjEnr2fMDvfQVv2QCA02hgu/12cH4iIMcwsMcnuDLhpFIJYmI0UCjkMBpNKCvTNkh9Nm/uvTcVNpsVS5cuqkrNjqxWCWq1ptYyLwDodDqfy7xqtQYWiwXmGq0vdDodGIaBWh1Zu+9J5KAluEaia9d7MGfOAuTlrcC2bV+iZctWmD79NbcHvwCftVT9t/vExERs2bIZX321BSaTCbfc0gJZWY9hzJhcjxlwngiekMAwMD2fC9VM/9lUxufHghWJBF1u80QiESMz8yFkZmYiPz8fmzZtQZ8+mYKOqab4+IRaz3qcyTK+lnmdPzt3rgR33tnBdbyk5CxatmxFy2/EKwpAjUhGRm9kZPT2ec7evcVur7OycpCVlVPvewudkGAaPgKyL7ZAXHzIYzICx7KwJSeDfSEXMTGasGe3BapmdtvEiVNQVFSEJUsWITX1fqhUUYKOr7r09Puxbt0a6HQ616ylsHAHWJZFamq61+u6dLkbKpUKhYU7XAHIZrPh++8LkZ7eo0HGThonWoIjAXEmJIjFYsjl0rDfz2y2wGq1ISqqatlPJkPF55tgHjwEnEgEjmHAicX8nywL6+ODwe7cCUXzmIhYbgM8dybVaJpj5MgxuH79Gj7+eJWg46spO/txKJVKzJjxKg4ePIBt277C8uVLkZ09yG0P0IQJL2Lo0BzXa5lMhmeeeRaffbYe//73Rvz00yH84x8zUVFRgaeeekaAT0IaC6qGTQLGMEDz5ioAQFlZcDv760IsFiE6Wg2DwehWIYG5fBmyb74Ge/0auNg4SIYOgTQxERaLle+yGgHLbe7ZbWa3BAOLxYLnnhuOmJhYLF36voAjre3s2f9h8eK3cfz4z1WleAZg7Fj3Ujzjx4/F5cuXsHnz165jfCmej/HFF5tRXl6GO+7ogFdemYwuXe4W4mMQLyKtGjYFIBKUSGrZUJdWCeEUTKsEs9kEgKmVukxIOEVaAKJnQCQogickQJjNpP4Eu5mUHswTQgGI1IFQCQkWixVyuTTistsiaTMpIY0JJSGQoAmRkGC3O6BWqwTdTFody7K0mbSaw4eLkZHR3WPtt/z87cjI6I7jx48KMDISySgAkTppqAoJzs2kLMvfxGg0C/6sx1N2W6RsJhVKcnI3tGjREgUF39b6WUHBt7jttjaUkEBqoQBE6iTcFRJYloVGo4JGEwW73YGyMm2tCgkNTSIRo1kzDVQqBRwODlqtERUVRmqLDYBhGDz88KPYv38v9Pob1RTKyspw8OABZGY+IuDoSKSiAETqzGSywWrlExLEYs/Vp+vCW+02Zz8dlUoRsnsFgpbbAtO//wBYLBbs3r3DdWzXrnzY7XY8/PCjAo6MRCoKQKRe9HoTOI67sWG0HvzVbnMmJIQ64PlCy22Bi49PQKdOScjP3+46lp+/HZ0734U2bdoKODISqSgAkXoJRUKCp+U2b6nVtSokhAktt9VN//4DcOTIYVy58gdKSy/gxIljtPxGvKIAROqtPgkJdWmV4LFlQ4jQclv99Ov3MFiWxY4d3yE//1uIxWL06xdZRVdJ5KB9QKTenAkJarUCKpUyoAoJ9dlMWr1lg9lsqVUhoa4ipTNpY9asWTOkp9+P7777FhaLGWlp96FZs2ZCD4tEKJoBkZAINCEhmOU2X0KZkEDLbaHVv/8AnDlzGufPn0NmJiUfEO9oBkRCxl+FhOq12+rbKsFTy4Zg1azdVllp8Vq7rSHs3fs98vJW4Pz5ErRo0QrDh4/CgAGP+bzm0qWLGDKk9jlJSV2wcuXHYRqpbz169IJarQHHOZCR0UuQMZDGgQIQCRlnQoJCIYVcLoXJZAEQvtptZrMFcrmsTiWBIm257eefj2DWrKnIysrGhAmv4qefDmHevDehVCrRp8+Dfq/PzR2H5OTurtdKZfi713rDMAxEIhF69HiAiq0SnygAkZAyGMyQycRQKOTYuXMnCgryMXPmTMjlsrDUbjMYKhEdrXYVR/UnUmu3rV37EZKSOmPq1JkAgJSU7igtvYBVqz4MKAC1adMWXbrcFe5hBmTPnt0oLy9D//4DhB4KiXAUgEhIcRxw6tRpLFmyEEVFRZBIJBg1ajSk0vBsHg00ISHSltuqs1gsOHy4GC+99Irb8QcfzMSOHd/h0qWLaN36VoFGF7gTJ47jzJnT+Pjjj9Chw1+QnNxN6CGRCEdJCCRkTCYTPvroAzzxxOMoKipCRkYGPv10IxISEsN6X38JCZG+mbS09AJsNhvatUtwOx4fz/+9lZSc9fseCxfOQ69eqcjKegjz5/8LWm1FGEbq29atm7Fw4TzExDTHa6/9s8HvTxofmgGRkOA4Drm5o3DmzO9o2bIVJk16FdnZWXA4HA3WsoFlAb1eh6govulWpC631aTTaQHANW4ntVoDANBqtV6vlUikyMkZjLS0dERFqXHy5HGsW7cap06dRF7eOojFDfdPfNasf2DWrH802P1I40cBiIQEwzDo3Pku9OrVB8OGjYRcLofZbKuVkBAuZrMFb731Jvbt24cNGzaibds2gi636fV6XL9+ze95t956W73uExcXhylT/uZ6nZzcDYmJ7TFt2kQUFRWiX7+H6vX+hIQTBSASMtOmzXJ77UxIUCoVVc9nwnv/u+66G9u2bUNe3oeYP3++oNlthYU7MH/+v/yet2HDZtdMx2DQu/3MOTPSaDRB3fu++3pAoVDg119/oQBEIhoFIBI2damQUFcSiRhPP/00vvnma2zduhVZWdno2FG4rLCBA3MwcGBOQOdaLBaIxWKUlJxFWtp9ruPOZz/x8QmhHyAhEYCSEEhYhatlg1P12m1SqQTTp88EwzCYN28ubLbIe97jiVQqRUpKd+zevdPt+K5dBUhISAw6A27fvj0wGo3o1CkplMMkJOQoAJGwC2XLhuo8ZbclJnZAVlYOfv/9N2zdujmk9wunkSOfw4kTx/DOO/Nw+HAxVq36EAUF2zF6dK7beb17p2Hu3Nmu18uWLcby5Uuxe/dOFBcfxCefrMHs2X9Hx45J6NnzgQb+FIQEh5bgSNh5q5BQV/6y23Jzx2Hv3iIcPfozBg9+sr7DbxBdu96DOXMWIC9vBbZt+xItW7bC9OmvoW9f902odrvdbTNvYmIitmzZjK++2gKTyYRbbmmBrKzHMGZMboNmwBFSFwwXYCnhq1fDm0pLbm4MAzRvrgIAlJVV1CkhoeZmUqPR6jW7TautgEQihULRsN1TCYlkt9yi9n9SA6JfkQgA4NChA9i27WucPHkcFy+WYtCgIZg8eXpA1+r1eixbtgjff78bNpsNaWnpmDhxGuLi4lzn1DchIdjabRpNdFDvTwhpePQMiAAADhz4Ab//fhr33JNSa0OkP6+/PgOHDv2IKVNm4I033sS5cyWYMuWVWkkAdUlIoFYJhNy8aAZEAADjxk3Ayy9PAgAcPlwc8HXHjx/FwYM/YNGi95Camg4AaNcuHsOGDfG4EdJfywanSK7dRggJDZoBEQD8F35dHDiwH1FRatx7b5rrWLt2Cbjzzg44cGBfrfOdCQlisRhyudTje0Z67TZCSGjQDIjUS0nJWbRrFw+GYdyOx8cnei2i6a1CQmOp3UYICQ0KQKRedDqtx2dGarXaaxHNmgkJlZUmWm4jpAmiAHSTCqYYpkQiaYARuTOZbJDL+YQEmUwaMZ1JCSENhwLQTSqYYpj1qTWmVmtw5coftY7rdDq/RTSdCQkcB+h0RlpuI6SJoQB0kwqmGGZ9xMcnoLj4IDiOc3sOVFJyFu3b3+HzWpuNQ3l5JWw2mvEQ0hRRFhypl/T0+6HTaVFcfNB17Ny5Epw+/SvS03v4vZ6CDyFNF82ACADg8uVL+OWXEwD41tqlpaUoLNwBAOjT50Y9st6909C//wDMmPE6AKBLl7uRmnof5s6djfHjJ0EqlSIv7320b38nevfu0/AfhBDSaFAAIgD4zadvvfVP1+sff9yPH3/cDwDYu/fGxtSaxTABYPbsuVi2bBEWLJgDu92O1NQ0TJo0LaKKYYa71BAhJHhUjJQ0CcuWLcaPP/6ApKTO+P773cjM7B9wAJo8+WWcPft/GDduImQyKVaufB8sK8JHH62LqCBLiD9UjJQQATRUqSFCSOAoCYE0CQ1VaogQEjgKQIT4UJdSQ4SQwFAAIsSHupQaIoQEhp4BkUYp0ksNEUL8owBEGqXGUGqIEOIbBSDSKDWGUkOEEN/oGRAhPtS31BAhxDuaAZEmgUoNERJ5KACRJuFmLzVESGNEpXgIIaSJiLRSPPQMiBBCiCAoABFCCBEEBSBCCCGCoABECCFEEBSACCGECIICECGEEEFQACKEECIICkCEEEIEQQGIEEKIICgAEUIIEQQFIEIIIYKgAEQIIUQQFIAIIYQIggIQIYQQQVAAIoQQIggKQIQQQgRBAYgQQoggAu6ISgghhIQSzYAIIYQIggIQIYQQQVAAIoQQIggKQIQQQgRBAYgQQoggKAARQggRBAUgQgghgqAARAghRBAUgAghhAji/wEYkioY72j8WAAAAABJRU5ErkJggg=="}}]}}}, "version_major": 2, "version_minor": 0} diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html index dd6a0dbe97..fb63eeb46f 100644 --- a/ml-fundamentals/classification/more-classifiers.html +++ b/ml-fundamentals/classification/more-classifiers.html @@ -2184,7 +2184,7 @@

    13.2.4. Exercise - apply logistic regres
    -
    Accuracy is 0.8115095913261051
+
    Accuracy is 0.8190158465387823
    @@ -2207,8 +2207,10 @@

    13.2.4. Exercise - apply logistic regres

    -
    ingredients: Index(['egg', 'honey', 'milk', 'shrimp', 'vegetable_oil', 'walnut'], dtype='object')
-cuisine: chinese
+
    ingredients: Index(['cayenne', 'cilantro', 'coconut', 'fish', 'lime_juice', 'peanut_butter',
+       'soy_sauce'],
+      dtype='object')
+cuisine: thai
    @@ -2257,25 +2259,25 @@

    13.2.4. Exercise - apply logistic regres + + thai + 0.993893 + chinese - 0.374274 + 0.003193 japanese - 0.252066 + 0.001686 korean - 0.207289 - - - thai - 0.128032 + 0.000869 indian - 0.038340 + 0.000360 @@ -2296,15 +2298,15 @@

    13.2.4. Exercise - apply logistic regres 
                  precision    recall  f1-score   support
 
-     chinese       0.73      0.73      0.73       232
-      indian       0.91      0.90      0.91       231
-    japanese       0.78      0.77      0.78       242
-      korean       0.84      0.79      0.82       243
-        thai       0.79      0.86      0.82       251
+     chinese       0.79      0.73      0.76       249
+      indian       0.95      0.89      0.92       239
+    japanese       0.79      0.78      0.78       224
+      korean       0.84      0.82      0.83       252
+        thai       0.75      0.89      0.81       235
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.82      1199
+   macro avg       0.82      0.82      0.82      1199
+weighted avg       0.82      0.82      0.82      1199
    diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html index 73fe281d2d..2e09078faf 100644 --- a/ml-fundamentals/classification/yet-other-classifiers.html +++ b/ml-fundamentals/classification/yet-other-classifiers.html @@ -1839,18 +1839,18 @@

    13.3.4.1. Exercise - apply a linear SVC<

    -
    Accuracy (train) for Linear SVC: 79.6% 
+
    Accuracy (train) for Linear SVC: 78.3% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.71      0.71       249
-      indian       0.86      0.89      0.88       238
-    japanese       0.84      0.74      0.78       250
-      korean       0.79      0.79      0.79       234
-        thai       0.77      0.87      0.82       228
+     chinese       0.66      0.66      0.66       238
+      indian       0.89      0.90      0.89       231
+    japanese       0.79      0.74      0.76       253
+      korean       0.82      0.77      0.80       230
+        thai       0.77      0.85      0.81       247
 
-    accuracy                           0.80      1199
-   macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
    @@ -1874,27 +1874,27 @@

    13.3.5.1. Exercise - apply the K-Neighbo

    -
    Accuracy (train) for Linear SVC: 79.6% 
+
    Accuracy (train) for Linear SVC: 78.3% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.71      0.71       249
-      indian       0.86      0.89      0.88       238
-    japanese       0.84      0.74      0.78       250
-      korean       0.79      0.79      0.79       234
-        thai       0.77      0.87      0.82       228
+     chinese       0.66      0.66      0.66       238
+      indian       0.89      0.90      0.89       231
+    japanese       0.79      0.74      0.76       253
+      korean       0.82      0.77      0.80       230
+        thai       0.77      0.85      0.81       247
 
-    accuracy                           0.80      1199
-   macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.2% 
+Accuracy (train) for KNN classifier: 73.3% 
               precision    recall  f1-score   support
 
-     chinese       0.66      0.64      0.65       249
-      indian       0.86      0.79      0.83       238
-    japanese       0.66      0.86      0.75       250
-      korean       0.86      0.56      0.68       234
-        thai       0.70      0.81      0.75       228
+     chinese       0.68      0.62      0.65       238
+      indian       0.83      0.83      0.83       231
+    japanese       0.66      0.87      0.75       253
+      korean       0.89      0.55      0.68       230
+        thai       0.71      0.78      0.74       247
 
     accuracy                           0.73      1199
    macro avg       0.75      0.73      0.73      1199
@@ -1926,45 +1926,45 @@ 
    13.3.6.1. Exercise - apply a Support Vec
 
    
 
    
 
    
-
    Accuracy (train) for Linear SVC: 79.6% 
+
    Accuracy (train) for Linear SVC: 78.3% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.71      0.71       249
-      indian       0.86      0.89      0.88       238
-    japanese       0.84      0.74      0.78       250
-      korean       0.79      0.79      0.79       234
-        thai       0.77      0.87      0.82       228
+     chinese       0.66      0.66      0.66       238
+      indian       0.89      0.90      0.89       231
+    japanese       0.79      0.74      0.76       253
+      korean       0.82      0.77      0.80       230
+        thai       0.77      0.85      0.81       247
 
-    accuracy                           0.80      1199
-   macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.2% 
+Accuracy (train) for KNN classifier: 73.3% 
               precision    recall  f1-score   support
 
-     chinese       0.66      0.64      0.65       249
-      indian       0.86      0.79      0.83       238
-    japanese       0.66      0.86      0.75       250
-      korean       0.86      0.56      0.68       234
-        thai       0.70      0.81      0.75       228
+     chinese       0.68      0.62      0.65       238
+      indian       0.83      0.83      0.83       231
+    japanese       0.66      0.87      0.75       253
+      korean       0.89      0.55      0.68       230
+        thai       0.71      0.78      0.74       247
 
     accuracy                           0.73      1199
    macro avg       0.75      0.73      0.73      1199
 weighted avg       0.75      0.73      0.73      1199
 
    
 
    
-
    Accuracy (train) for SVC: 82.0% 
+
    Accuracy (train) for SVC: 80.3% 
               precision    recall  f1-score   support
 
-     chinese       0.75      0.72      0.74       249
-      indian       0.90      0.89      0.89       238
-    japanese       0.87      0.80      0.83       250
-      korean       0.82      0.83      0.82       234
-        thai       0.77      0.86      0.81       228
+     chinese       0.69      0.69      0.69       238
+      indian       0.89      0.94      0.91       231
+    japanese       0.80      0.77      0.79       253
+      korean       0.83      0.75      0.79       230
+        thai       0.81      0.87      0.84       247
 
-    accuracy                           0.82      1199
-   macro avg       0.82      0.82      0.82      1199
-weighted avg       0.82      0.82      0.82      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
    
 
    
 
    
@@ -1989,73 +1989,73 @@ 
    13.3.7. Ensemble Classifiers
-
    Accuracy (train) for Linear SVC: 79.6% 
+
    Accuracy (train) for Linear SVC: 78.3% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.71      0.71       249
-      indian       0.86      0.89      0.88       238
-    japanese       0.84      0.74      0.78       250
-      korean       0.79      0.79      0.79       234
-        thai       0.77      0.87      0.82       228
+     chinese       0.66      0.66      0.66       238
+      indian       0.89      0.90      0.89       231
+    japanese       0.79      0.74      0.76       253
+      korean       0.82      0.77      0.80       230
+        thai       0.77      0.85      0.81       247
 
-    accuracy                           0.80      1199
-   macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.2% 
+Accuracy (train) for KNN classifier: 73.3% 
               precision    recall  f1-score   support
 
-     chinese       0.66      0.64      0.65       249
-      indian       0.86      0.79      0.83       238
-    japanese       0.66      0.86      0.75       250
-      korean       0.86      0.56      0.68       234
-        thai       0.70      0.81      0.75       228
+     chinese       0.68      0.62      0.65       238
+      indian       0.83      0.83      0.83       231
+    japanese       0.66      0.87      0.75       253
+      korean       0.89      0.55      0.68       230
+        thai       0.71      0.78      0.74       247
 
     accuracy                           0.73      1199
    macro avg       0.75      0.73      0.73      1199
 weighted avg       0.75      0.73      0.73      1199
 
    
 
    
-
    Accuracy (train) for SVC: 82.0% 
+
    Accuracy (train) for SVC: 80.3% 
               precision    recall  f1-score   support
 
-     chinese       0.75      0.72      0.74       249
-      indian       0.90      0.89      0.89       238
-    japanese       0.87      0.80      0.83       250
-      korean       0.82      0.83      0.82       234
-        thai       0.77      0.86      0.81       228
+     chinese       0.69      0.69      0.69       238
+      indian       0.89      0.94      0.91       231
+    japanese       0.80      0.77      0.79       253
+      korean       0.83      0.75      0.79       230
+        thai       0.81      0.87      0.84       247
 
-    accuracy                           0.82      1199
-   macro avg       0.82      0.82      0.82      1199
-weighted avg       0.82      0.82      0.82      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
    
 
    
-
    Accuracy (train) for RFST: 84.5% 
+
    Accuracy (train) for RFST: 82.4% 
               precision    recall  f1-score   support
 
-     chinese       0.82      0.77      0.79       249
-      indian       0.89      0.89      0.89       238
-    japanese       0.90      0.82      0.86       250
-      korean       0.82      0.87      0.84       234
-        thai       0.80      0.88      0.84       228
+     chinese       0.77      0.74      0.76       238
+      indian       0.90      0.93      0.91       231
+    japanese       0.85      0.79      0.82       253
+      korean       0.81      0.77      0.79       230
+        thai       0.79      0.89      0.84       247
 
-    accuracy                           0.84      1199
-   macro avg       0.85      0.85      0.85      1199
-weighted avg       0.85      0.84      0.84      1199
+    accuracy                           0.82      1199
+   macro avg       0.82      0.82      0.82      1199
+weighted avg       0.82      0.82      0.82      1199
 
    
 
    
-
    Accuracy (train) for ADA: 70.7% 
+
    Accuracy (train) for ADA: 70.2% 
               precision    recall  f1-score   support
 
-     chinese       0.61      0.46      0.52       249
-      indian       0.87      0.85      0.86       238
-    japanese       0.71      0.58      0.64       250
-      korean       0.62      0.82      0.71       234
-        thai       0.74      0.84      0.79       228
+     chinese       0.59      0.53      0.56       238
+      indian       0.84      0.81      0.83       231
+    japanese       0.75      0.60      0.67       253
+      korean       0.65      0.77      0.70       230
+        thai       0.69      0.80      0.74       247
 
-    accuracy                           0.71      1199
-   macro avg       0.71      0.71      0.70      1199
-weighted avg       0.71      0.71      0.70      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/regression/linear-and-polynomial-regression.html b/ml-fundamentals/regression/linear-and-polynomial-regression.html
index 99db642e11..a4a036a829 100644
--- a/ml-fundamentals/regression/linear-and-polynomial-regression.html
+++ b/ml-fundamentals/regression/linear-and-polynomial-regression.html
@@ -2213,7 +2213,7 @@ 
    11.3.3. Correlation
 
    
 
    
-
    <matplotlib.collections.PathCollection at 0x7f82242c05b0>
+
    <matplotlib.collections.PathCollection at 0x7fb1660cd760>
 
    
 
    
 ../../_images/linear-and-polynomial-regression_12_1.png
@@ -2399,7 +2399,7 @@ 
    11.3.5. Simple Linear Regression
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x7f82195fd5b0>]
+
    [<matplotlib.lines.Line2D at 0x7fb15b38b8b0>]
 
    
 
    
 ../../_images/linear-and-polynomial-regression_34_1.png
diff --git a/ml-fundamentals/regression/logistic-regression.html b/ml-fundamentals/regression/logistic-regression.html
index 3d8c6bc6aa..45af45957a 100644
--- a/ml-fundamentals/regression/logistic-regression.html
+++ b/ml-fundamentals/regression/logistic-regression.html
@@ -2099,7 +2099,7 @@ 
    11.4.5.1. Visualization - side-by-side g
 
    
 
    
 
    
-
    <seaborn.axisgrid.PairGrid at 0x7efbe7287880>
+
    <seaborn.axisgrid.PairGrid at 0x7f2a67a3eb80>
 
    
 
    
 ../../_images/logistic-regression_6_1.png
@@ -2127,7 +2127,9 @@ 
    11.4.5.2. Use a swarm plot
 
    /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
   warnings.warn(msg, UserWarning)
-/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
+
    
+
    
+
    /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
   warnings.warn(msg, UserWarning)
 
    
 
    
@@ -2140,7 +2142,7 @@ 
    11.4.5.2. Use a swarm plot
+../../_images/logistic-regression_8_4.png
 
    
 
    
 
@@ -2158,7 +2160,7 @@ 
    11.4.5.3. Violin plot
-
    <seaborn.axisgrid.FacetGrid at 0x7efbda001c70>
+
    <seaborn.axisgrid.FacetGrid at 0x7f2a67a646d0>
 
    
 
    
 ../../_images/logistic-regression_10_1.png
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5,76,77,78,80,82,85,88,89,90,92,93,99,100,101,102,104,106,108,109,110,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,135,136,137,139,140,141,142,144,145,146,147,148,150,151,155,156,158,159,162,165,167,168,170,171,172,173,176,177,179,180,182,184,187,188,191,192,193,194,195],AND:[92,93,108,119,122,171,172],AS:[22,25,45,47,48,92,93,171,172],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,129,130,133,136,139,140,141,142,143,147,156,158,168,172,176,180,184,187,194],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,123,124,132,133,134,139,140,141,147,150,151,155,156,157,158,162,165,166,167,168,171,172,173,177,179,182,191,193,194],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,130,140,141,143,147,151,152,157,165,168,170,171,172,179,191,192],BE:[92,93,171,172],BUT:[92,93,171,172],BY:[101,141,146],Be:[85,91,104,108,119,159],Being:[43,62,101,104,123],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,127,129,131,137,139,140,141,143,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,109,114,117,119,123,124,128,137,139,140,142,145,146,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,121,122,123,126,129,130,131,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,128,129,130,131,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,143,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,171,172],ON:[124,180],ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,121,122,123,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,128,131,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,121,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,122,123,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,121,123,124,127,128,129,131,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,122,123,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[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__:171,__annotations__:[171,193],__builtins__:171,__cached__:171,__call__:[63,65,128,133],__class__:[37,171],__dict__:[189,190],__doc__:[171,193],__eq__:92,__file__:[127,171],__finalize__:[121,122],__future__:[37,127],__get__:121,__getitem__:[122,123],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,128,130,132,133,134,135,152,171,188,189,193],__iter__:33,__len__:33,__loader__:171,__main__:[35,127,130,159],__mul__:92,__name__:[35,37,127,130,159,171],__operators__:134,__package__:171,__repr__:55,__spec__:171,__str__:92,__sub__:92,__truediv__:92,__version__:[41,158,193],_aspp:133,_attach:[118,180],_bin:54,_branch:133,_build_model:35,_bunch:[57,58],_caller:121,_check_indexing_error:123,_check_params_vs_input:146,_concaten:121,_consolidate_inplac:122,_constructor:122,_conv_block:133,_conv_bn_relu:133,_conv_relu:133,_data:122,_deeplabv3:133,_deprecate_mismatched_index:122,_deprecated_arg:122,_engin:123,_etag:[118,180],_fcn_16:133,_fcn_32:133,_fcn_8:133,_fuse_bn_tensor:132,_get_axi:122,_get_block_manager_axi:122,_get_comb_axi:121,_get_concat_axi:121,_get_join_info:121,_get_list_axi:122,_get_result_dim:121,_get_slice_axi:122,_get_valu:[122,123],_get_values_for_loc:123,_getbool_axi:122,_getitem_axi:122,_getitem_lowerdim:122,_getitem_tupl:122,_i:[77,78,148,158],_identity_block:133,_ilocindex:122,_index:56,_indicator_pre_merg:121,_info_axi:121,_invalid_index:122,_is_copi:122,_is_scalar_access:122,_items_overlap_with_suffix:121,_j:[148,158],_k:130,_kmean:146,_label:57,_left:121,_lib:[121,123],_locationindex:122,_locindex:122,_m:130,_make_concat_multiindex:121,_make_stag:132,_maybe_cast_for_get_loc:122,_maybe_cast_slice_bound:122,_maybe_check_integr:121,_merge_doc:121,_merge_typ:121,_mergeoper:121,_method:179,_mgr:[121,122],_novalu:179,_other:121,_pad_1x1_to_3x3_tensor:132,_pickl:127,_recognized_scalar:122,_reindex_and_concat:121,_rid:[118,180],_right:121,_sec_1:93,_segnet:133,_self:[118,180],_sigmoid:[81,189],_skip:3,_slice:122,_static:[122,123],_subplot:74,_sum:179,_t:[118,180],_t_sne:186,_take:122,_take_with_is_copi:122,_takeabl:122,_valid_typ:122,_validate_integ:122,_validate_kei:122,_validate_tuple_index:122,_valu:123,a0958ad901d7:118,a0:[121,179],a10:123,a1:[119,121,179],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:172,a23:[171,193],a2:[119,121,179],a3:121,a3z5kdkfn3tbq:59,a4:121,a5:121,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:82,a_dict:172,a_i:[82,147],a_list:172,a_n:150,aaaaaa:[156,184],aafter:157,aaron:[29,50,78,131],ab:[50,63,65,77,92,93,122,127,128,135,158,171,172,194],abadi:131,abbeel:128,abbrevi:[124,128],abc:[93,122,123,172,179,195],abcd:[7,117,122,179],abcdef:122,abcmous:[112,176],abhinav:[139,143],abil:[43,52,54,68,76,77,80,108,129,139,140,150,156,159,165,171,173,184,191],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,123,129,137,140,142,145,151,154,157,159,162,165,166,167,168,170,176,186,189,190,193],abnorm:29,abnorml:66,abo:38,aboslut:157,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,121,123,124,125,129,130,133,135,137,138,139,140,141,142,143,144,145,146,147,150,151,152,154,155,157,158,159,161,163,164,165,166,168,169,170,171,172,173,174,176,177,180,188,191,193,195],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,121,122,123,124,127,128,129,130,132,133,135,137,139,140,141,142,145,146,147,148,150,154,155,157,158,162,165,166,167,168,169,170,171,172,173,178,187,188],above_cutoff:158,abracadabra:172,abraham:195,abs_vector:[172,194],absenc:[54,186],absent:122,absolut:[47,76,78,83,92,116,119,141,150,154,157,171,172,173,194],absolute_error:77,absolute_import:127,absolute_percentage_error:77,abspath:127,abund:[110,178],ac:158,academ:[112,115,138,176],academi:193,acc:[33,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163,171],compat:[15,55,101,119,122,130,131,134,136,138],compatible_format:193,compel:78,compens:[155,158],compet:151,competit:[129,137,143,151,154,155],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,133,140,173,179,182],compilaton:40,complaint:[104,112,176],compleletli:[68,80],complementari:137,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,121,123,127,128,132,135,136,137,139,141,143,155,158,165,166,167,168,171,172,173,175,186,193,194],complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,129,132,135,137,138,139,140,141,143,147,153,156,157,158,162,165,171,173,181,182,184,185,192,195],complex_numb:171,complex_number_1:[172,194],complex_number_2:[172,194],complex_number__1:194,complexnumb:171,complexnumberwithconstructor:171,compli:112,complianc:[22,45,47,48,112,176],compliant:112,complic:[36,50,82,114,119,139,140,141,151,154,157,158,168,182],compon:[75,76,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,127,131,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,123,141,166],compress:[29,30,31,106,126,129,132],compris:[39,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,121,122,123,126,127,128,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,122,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concat_axi:121,concat_index:121,concaten:[34,38,55,76,119,121,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,123,127,131,132,138,139,140,141,142,148,151,155,156,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_conv:135,conf_matrix:[52,57],confer:[104,108,121,139,143],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,123],conform:[113,123,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,127,132,133,136,141,157,171,172,182],connectionist:78,connor:139,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[59,101,106,112,114,115,123,151,154,158,176,177],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,165,166,168,172,176,177,180,191],consol:123,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,132,136,141,151,155],constant_initi:127,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,163,186],construct:[30,50,119,123,126,128,132,137,139,145,147,150,151,152,155,171,172,194],constructor:[123,132,171,172,193,194],consult:[7,121],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,159,165,175],consumpt:[108,159,175,179],cont:54,cont_num_var:54,contact:[118,180,187],contagi:167,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,121,123,124,129,132,133,136,139,140,141,145,147,150,152,155,158,159,162,165,166,167,168,170,171,172,179,193,194],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,134,135,136,137,138,141,143,145,146,147,148,150,1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Self-paced assignments","37.22. Analyzing COVID-19 papers","37.27. Analyzing data","37.9. Analyzing text about Data Science","37.13. Apply your skills","37.16. Build your own custom vis","37.17. Classifying datasets","37.26. Data preparation","37.24. Data processing in Python","37.41. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","37.40. Data Science project using Azure ML SDK","37.10. Data Science scenarios","37.20. Displaying airport data","37.15. Dive into the beehive","37.23. Estimation of COVID-19 pandemic","37.25. Evaluating data from a form","37.36. Explore a planetary computer dataset","37.37. Exploring for answers","37.19. Introduction to probability and statistics","37.12. Lines, scatters and bars","37.39. Low code/no code Data Science project on Azure ML","37.38. Market research","37.29. Matplotlib applied","37.28. NYC taxi data in winter and summer","37.18. Small diabetes study","37.21. Soda profits","37.35. Tell a story","37.14. Try it in Excel","37.11. Write a data ethics case study","37.101. Intro to Autoencoders","37.102. Base/Denoising Autoencoder & Dimension Reduction","37.103. Fun with Variational Autoencoders","37.90. How to choose cnn architecture mnist","37.94. Object Recognition in Images using CNN","37.92. Sign Language Digits Classification with CNN","37.112. DQN On Foreign Exchange Market","37.113. Art by gan","37.115. Generative Adversarial Networks (GANs)","37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment","37.107. NN Classify 15 Fruits Assignment","37.106. Neural Networks for Classification with TensorFlow","37.116. Basic classification: Classify images of clothing","37.99. Google Stock Price Prediction RNN","37.95. Intro to TensorFlow for Deep Learning","37.104. Time Series Forecasting Assignment","37.83. Counterintuitive Challenges in ML Debugging","37.82. Data engineering","37.84. Case Study: Debugging in Classification","37.85. Case Study: Debugging in Regression","37.75. Beyond random forests: more ensemble models","37.76. Decision trees","37.80. Random Forest Classifier with Feature Importance","37.74. Random forests for classification","37.73. Random forests intro and regression","37.79. Boosting with tuning","37.78. Gradient boosting","37.77. Hyperparameter tuning gradient boosting","37.67. Decision Trees - Classification","37.66. Decision Trees - Intro and Regression","37.63. Kernel method assignment 1","37.65. Support Vector Machines (SVM) - Classification","37.64. Support Vector Machines (SVM) - Intro and SVM for Regression","37.70. Dropout and Batch Normalization","37.71. Lasso and Ridge Regression","37.69. Learning Curve To Identify Overfit & Underfit","37.68. Model selection assignment 1","37.72. Regularized Linear Models","37.88. Build Classification Model","37.87. Build classification models","37.57. Create a regression model","37.61. Delicious asian and indian cuisines","37.62. Explore classification methods","37.55. Exploring visualizations","37.58. Linear and polynomial regression","37.45. Linear regression - California Housing","37.48. Gradient descent","37.49. Linear Regression Implementation from Scratch","37.46. Linear Regression Metrics","37.47. Loss Function","37.54. Managing data","37.50. ML logistic regression - assignment 1","37.51. ML logistic regression - assignment 2","37.52. ML neural network - Assignment 1","37.42. Machine Learning overview - assignment 1","37.43. Machine Learning overview - assignment 2","37.89. Parameter play","37.60. Pumpkin varieties and color","37.53. Regression tools","37.44. Regression with Scikit-learn","37.59. Retrying some regression","37.86. Study the solvers","37.56. Try a different model","37.8. Python programming advanced","37.7. Python programming basics","37.6. Python programming introduction","37.5. Project Plan\u200b Template","37.3. First assignment","37.4. 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. Pandas","5.4.3. Advanced Pandas Techniques","5.4.2. Data Selection","5.4.1. Introduction and Data Structures","5.1. Relational databases","5. Working with data","24. Autoencoder","21. Convolutional Neural Networks","30. Diffusion Model","20. Intro to Deep Learning","27. Deep Q-learning","22. Generative adversarial networks","28. Image classification","29. Image segmentation","25. Long-short term memory","31. Object detection","23. Recurrent Neural Networks","26. Time series","Learn AI together, for free","34. Data engineering","36. Model deployment","35. Model training & evaluation","32. Overview","33. Problem framing","14. Clustering models for Machine Learning","14.1. Introduction to clustering","14.2. K-Means clustering","15.1. Bagging","15.3. Feature importance","15. Getting started with ensemble learning","15.2. Random forest","16.1. Gradient Boosting","16.2. Gradient boosting example","16. Introduction to Gradient Boosting","16.3. XGBoost","16.4. XGBoost + k-fold CV + Feature Importance","18. Kernel method","19. Model selection","17. Unsupervised learning","12. Build a web app to use a Machine Learning model","13.4. Applied Machine Learning : build a web app","13. Getting started with classification","13.1. Introduction to classification","13.2. More classifiers","13.3. Yet other classifiers","10. Machine Learning overview","11.3. Linear and polynomial regression","11.4. Logistic regression","11.2. Managing data","11. Regression models for Machine Learning","11.1. Tools of the trade","3. Python programming advanced","2. Python programming basics","1. Python programming introduction","38.10. Data Science in real world","38.9. Data Science in the cloud","38.4. Data Science introduction","38.8. Data Science lifecycle","38.7. Data visualization","38.6. NumPy and Pandas","38.5. Relational vs. non-relational database","38.20. Convolutional Neural Network","38.21. Generative Adversarial Network","38. Slides","38.18. Kernel method","38.19. Model Selection","38.17. Unsupervised learning","38.16. Build an machine learning web application","38.12. Linear Regression","38.13. Logistic Regression","38.14. Logistic Regression","38.11. Machine Learning overview","38.15. Neural Network","38.3. Python programming advanced","38.2. Python programming basics","38.1. Python programming 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,187,191,192,194],"y\u00fcksek":35,A:[0,1,4,5,6,7,12,13,14,15,18,19,21,23,26,28,29,32,36,40,41,43,45,47,48,49,50,51,52,56,57,58,59,62,63,64,65,66,68,72,74,75,76,77,78,80,82,85,88,89,90,92,93,99,100,101,102,104,106,108,109,110,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,135,136,137,139,140,141,142,144,145,146,147,148,150,151,155,156,158,159,162,165,167,168,170,171,172,173,176,177,179,180,182,184,187,188,191,192,193,194,195],AND:[92,93,108,119,122,171,172],AS:[22,25,45,47,48,92,93,171,172],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,129,130,133,136,139,140,141,142,143,147,156,158,168,172,176,180,184,187,194],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,123,124,132,133,134,139,140,141,147,150,151,155,156,157,158,162,165,166,167,168,171,172,173,177,179,182,191,193,194],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,130,140,141,143,147,151,152,157,165,168,170,171,172,179,191,192],BE:[92,93,171,172],BUT:[92,93,171,172],BY:[101,141,146],Be:[85,91,104,108,119,159],Being:[43,62,101,104,123],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,127,129,131,137,139,140,141,143,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,109,114,117,119,123,124,128,137,139,140,142,145,146,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,121,122,123,126,129,130,131,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,128,129,130,131,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,143,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,171,172],ON:[124,180],ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,121,122,123,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,128,131,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,121,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,122,123,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,121,123,124,127,128,129,131,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,122,123,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[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__:171,__annotations__:[171,193],__builtins__:171,__cached__:171,__call__:[63,65,128,133],__class__:[37,171],__dict__:[189,190],__doc__:[171,193],__eq__:92,__file__:[127,171],__finalize__:[121,122],__future__:[37,127],__get__:121,__getitem__:[122,123],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,128,130,132,133,134,135,152,171,188,189,193],__iter__:33,__len__:33,__loader__:171,__main__:[35,127,130,159],__mul__:92,__name__:[35,37,127,130,159,171],__operators__:134,__package__:171,__repr__:55,__spec__:171,__str__:92,__sub__:92,__truediv__:92,__version__:[41,130,158,193],_aspp:133,_attach:[118,180],_bin:54,_branch:133,_build_model:35,_bunch:[57,58],_caller:121,_check_indexing_error:123,_check_params_vs_input:146,_concaten:121,_consolidate_inplac:122,_constructor:122,_conv_block:133,_conv_bn_relu:133,_conv_relu:133,_data:122,_deeplabv3:133,_deprecate_mismatched_index:122,_deprecated_arg:122,_engin:123,_etag:[118,180],_fcn_16:133,_fcn_32:133,_fcn_8:133,_fuse_bn_tensor:132,_get_axi:122,_get_block_manager_axi:122,_get_comb_axi:121,_get_concat_axi:121,_get_join_info:121,_get_list_axi:122,_get_result_dim:121,_get_slice_axi:122,_get_valu:[122,123],_get_values_for_loc:123,_getbool_axi:122,_getitem_axi:122,_getitem_lowerdim:122,_getitem_tupl:122,_i:[77,78,148,158],_identity_block:133,_ilocindex:122,_index:56,_indicator_pre_merg:121,_info_axi:121,_invalid_index:122,_is_copi:122,_is_scalar_access:122,_items_overlap_with_suffix:121,_j:[148,158],_k:130,_kmean:146,_label:57,_left:121,_lib:[121,123],_locationindex:122,_locindex:122,_m:130,_make_concat_multiindex:121,_make_stag:132,_maybe_cast_for_get_loc:122,_maybe_cast_slice_bound:122,_maybe_check_integr:121,_merge_doc:121,_merge_typ:121,_mergeoper:121,_method:179,_mgr:[121,122],_novalu:179,_other:121,_pad_1x1_to_3x3_tensor:132,_pickl:127,_recognized_scalar:122,_reindex_and_concat:121,_rid:[118,180],_right:121,_sec_1:93,_segnet:133,_self:[118,180],_sigmoid:[81,189],_skip:3,_slice:122,_static:[122,123],_subplot:74,_sum:179,_t:[118,180],_t_sne:186,_take:122,_take_with_is_copi:122,_takeabl:122,_valid_typ:122,_validate_integ:122,_validate_kei:122,_validate_tuple_index:122,_valu:123,_vendor:130,a0958ad901d7:118,a0:[121,179],a10:123,a1:[119,121,179],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:172,a23:[171,193],a2:[119,121,179],a3:121,a3z5kdkfn3tbq:59,a4:121,a5:121,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:82,a_dict:172,a_i:[82,147],a_list:172,a_n:150,aaaaaa:[156,184],aafter:157,aaron:[29,50,78,131],ab:[50,63,65,77,92,93,122,127,128,135,158,171,172,194],abadi:131,abbeel:128,abbrevi:[124,128],abc:[93,122,123,172,179,195],abcd:[7,117,122,179],abcdef:122,abcmous:[112,176],abhinav:[139,143],abil:[43,52,54,68,76,77,80,108,129,139,140,150,156,159,165,171,173,184,191],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,123,129,137,140,142,145,151,154,157,159,162,165,166,167,168,170,176,186,189,190,193],abnorm:29,abnorml:66,abo:38,aboslut:157,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,121,123,124,125,129,130,133,135,137,138,139,140,141,142,143,144,145,146,147,150,151,152,154,155,157,158,159,161,163,164,165,166,168,169,170,171,172,173,174,176,177,180,188,191,193,195],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,121,122,123,124,127,128,129,130,132,133,135,137,139,140,141,142,145,146,147,148,150,154,155,157,158,162,165,166,167,168,169,170,171,172,173,178,187,188],above_cutoff:158,abracadabra:172,abraham:195,abs_vector:[172,194],absenc:[54,186],absent:122,absolut:[47,76,78,83,92,116,119,141,150,154,157,171,172,173,194],absolute_error:77,absolute_import:127,absolute_percentage_error:77,abspath:127,abund:[110,178],ac:158,academ:[112,115,138,176],academi:193,acc:[33,39,47,49,52,57,127,136,192],acc_and_loss:127,acc_output:127,acceler:[100,110,111,141,143,166,178],acceleromet:116,accept:[16,40,57,68,80,83,100,103,106,112,119,122,123,132,141,143,151,155,165,171,177,191,192],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,122,139,142,143,159,166,171,172,176,177,179,187,193,194],accessor:123,accident:151,acclaim:155,accommod:[7,36,47,117,172],accompani:[116,141,166],accomplish:[88,141,151,165,186,191],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,122,132,139,142,145,147,150,155,159,163,165,166,167,168,170,186],accordingli:[41,55,127,145,171,193],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,147,151,166,167,176,179],accumul:[1,50,92,106,137,148,165,172,191],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,127,129,133,135,139,142,143,147,154,157,162,165,166,167,177],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,133,136,141,142,146,147,148,150,151,153,154,156,158,159,163,164,166,167,174,176,182,186],accuracy_of_batch:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,150,155,159,163,164,167,186,189,190],achiev:[32,33,40,48,50,54,56,59,103,106,119,130,131,132,133,139,140,141,142,150,151,152,153,155,156,157,158,171],aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,139,176],acoust:[144,145,146],acquir:[6,103,106,141,177],acquisit:[3,102,106,114,137,174,176],acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,121,123,124,127,137,139,140,141,143,151,155,158,171,172,174,176],act:[3,14,22,24,35,53,62,93,104,112,119,123,127,130,135,165,166,179,191],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,131,140,142,159,163,165,171,172,176,191],action_prob:130,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,142,151,157,158,162,176,181,182,187,192],activateion:134,activespac:180,actor:171,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,122,123,127,128,129,137,139,145,151,153,155,157,158,162,165,166,167,171,173,175,177,181,188,191],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:150,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,127,128,132,138,140,141,150,151,152,154,156,165,166,172,188,194],ada:164,adaboost:[151,164],adaboost_clf:49,adaboostclassifi:[49,56,163,164],adagradoptim:141,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,128,132,133,134,182],adamax:36,adamharlei:181,adamoptim:[127,131,141],adapt:[47,58,62,99,112,132,137,141,151,176,192],adaptiveaveragepooling2d:132,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,121,124,126,127,128,132,133,134,135,140,150,151,152,154,155,156,157,159,164,165,166,167,168,170,171,172,173,180,182,184,187,188,191,192,194],add_1:134,add_:31,add_artist:[110,178],add_legend:145,add_selectbox:187,add_slid:187,add_subplot:[35,37,47,75,130],add_trick:171,add_weight:132,addison:140,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,130,132,133,134,135,137,141,143,147,151,152,154,155,156,158,166,172,173,179,180,194,195],addition:[31,117,119,124,140,145,147,151,156,180,184],additon:32,addon:128,address:[75,76,90,103,104,106,112,137,139,140,143,147,154,165,171,176,177],adel:150,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,141],adher:[48,106],adjac:171,adject:[172,194],adjunct:165,adjust:[29,33,36,45,55,57,58,75,132,141,150,151,156,164],admin2:142,admin:180,administr:[143,173],admonit:[121,122,123],adobe_premier:38,adolesc:171,adopt:[59,85,102,112,140,141,143,147,151,174,176,195],adult:[171,193],advanc:[35,43,66,102,112,116,120,123,125,138,140,141,143,147,165,172,180,183],advantag:[40,50,54,68,80,123,132,140,143,150,153,154,156,172],advent:[49,129,140],advers:28,adversari:[36,143,183],advertis:104,advic:141,advis:[7,46,53,58,61,117,150],advoc:112,ae:[31,128],aebf:[118,180],aeroplan:7,aerospik:180,aesthet:22,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,130,132,141,148,154,156,157,171,174,176,184],affer:136,affin:[82,145],affinity_matrix_:158,afford:[7,74,117,165],african:[112,143,176],afro:[145,146],afropop:[145,146],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,130,132,133,134,136,137,140,141,142,145,146,148,150,151,154,155,159,164,165,166,168,171,172,173,187,189,190,192,193,194],afterward:[32,119],afzal:139,ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,119,121,140,145,147,148,152,155,162,165,169,170,171,172,173,179,180,189,190,191,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],company_s:56,company_typ:56,companyx:172,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,122,126,128,132,140,141,142,147,148,150,155,163,165,167,172,191,194],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,141,147,151,156,163,171],compat:[15,55,101,119,122,130,131,134,136,138],compatible_format:193,compel:78,compens:[155,158],compet:151,competit:[129,137,143,151,154,155],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,133,140,173,179,182],compilaton:40,complaint:[104,112,176],compleletli:[68,80],complementari:137,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,121,123,127,128,132,135,136,137,139,141,143,155,158,165,166,167,168,171,172,173,175,186,193,194],complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,129,132,135,137,138,139,140,141,143,147,153,156,157,158,162,165,171,173,181,182,184,185,192,195],complex_numb:171,complex_number_1:[172,194],complex_number_2:[172,194],complex_number__1:194,complexnumb:171,complexnumberwithconstructor:171,compli:112,complianc:[22,45,47,48,112,176],compliant:112,complic:[36,50,82,114,119,139,140,141,151,154,157,158,168,182],compon:[75,76,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,127,131,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,123,141,166],compress:[29,30,31,106,126,129,132],compris:[39,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,121,122,123,126,127,128,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,122,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concat_axi:121,concat_index:121,concaten:[34,38,55,76,119,121,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,123,127,131,132,138,139,140,141,142,148,151,155,156,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_conv:135,conf_matrix:[52,57],confer:[104,108,121,139,143],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,123],conform:[113,123,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,127,130,132,133,136,141,157,171,172,182],connectionist:78,connecttimeouterror:130,connor:139,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[59,101,106,112,114,115,123,151,154,158,176,177],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,165,166,168,172,176,177,180,191],consol:123,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,132,136,141,151,155],constant_initi:127,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,163,186],construct:[30,50,119,123,126,128,132,137,139,145,147,150,151,152,155,171,172,194],constructor:[123,132,171,172,193,194],consult:[7,121],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,159,165,175],consumpt:[108,159,175,179],cont:54,cont_num_var:54,contact:[118,180,187],contagi:167,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,121,123,124,129,132,133,136,139,140,141,145,147,149,150,152,155,158,159,162,165,166,167,168,170,171,172,179,193,194],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,1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Self-paced assignments","37.22. Analyzing COVID-19 papers","37.27. Analyzing data","37.9. Analyzing text about Data Science","37.13. Apply your skills","37.16. Build your own custom vis","37.17. Classifying datasets","37.26. Data preparation","37.24. Data processing in Python","37.41. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","37.40. Data Science project using Azure ML SDK","37.10. Data Science scenarios","37.20. Displaying airport data","37.15. Dive into the beehive","37.23. Estimation of COVID-19 pandemic","37.25. Evaluating data from a form","37.36. Explore a planetary computer dataset","37.37. Exploring for answers","37.19. Introduction to probability and statistics","37.12. Lines, scatters and bars","37.39. Low code/no code Data Science project on Azure ML","37.38. Market research","37.29. Matplotlib applied","37.28. NYC taxi data in winter and summer","37.18. Small diabetes study","37.21. Soda profits","37.35. Tell a story","37.14. Try it in Excel","37.11. Write a data ethics case study","37.101. Intro to Autoencoders","37.102. Base/Denoising Autoencoder & Dimension Reduction","37.103. Fun with Variational Autoencoders","37.90. How to choose cnn architecture mnist","37.94. Object Recognition in Images using CNN","37.92. Sign Language Digits Classification with CNN","37.112. DQN On Foreign Exchange Market","37.113. Art by gan","37.115. Generative Adversarial Networks (GANs)","37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment","37.107. NN Classify 15 Fruits Assignment","37.106. Neural Networks for Classification with TensorFlow","37.116. Basic classification: Classify images of clothing","37.99. Google Stock Price Prediction RNN","37.95. Intro to TensorFlow for Deep Learning","37.104. Time Series Forecasting Assignment","37.83. Counterintuitive Challenges in ML Debugging","37.82. Data engineering","37.84. Case Study: Debugging in Classification","37.85. Case Study: Debugging in Regression","37.75. Beyond random forests: more ensemble models","37.76. Decision trees","37.80. Random Forest Classifier with Feature Importance","37.74. Random forests for classification","37.73. Random forests intro and regression","37.79. Boosting with tuning","37.78. Gradient boosting","37.77. Hyperparameter tuning gradient boosting","37.67. Decision Trees - Classification","37.66. Decision Trees - Intro and Regression","37.63. Kernel method assignment 1","37.65. Support Vector Machines (SVM) - Classification","37.64. Support Vector Machines (SVM) - Intro and SVM for Regression","37.70. Dropout and Batch Normalization","37.71. Lasso and Ridge Regression","37.69. Learning Curve To Identify Overfit & Underfit","37.68. Model selection assignment 1","37.72. Regularized Linear Models","37.88. Build Classification Model","37.87. Build classification models","37.57. Create a regression model","37.61. Delicious asian and indian cuisines","37.62. Explore classification methods","37.55. Exploring visualizations","37.58. Linear and polynomial regression","37.45. Linear regression - California Housing","37.48. Gradient descent","37.49. Linear Regression Implementation from Scratch","37.46. Linear Regression Metrics","37.47. Loss Function","37.54. Managing data","37.50. ML logistic regression - assignment 1","37.51. ML logistic regression - assignment 2","37.52. ML neural network - Assignment 1","37.42. Machine Learning overview - assignment 1","37.43. Machine Learning overview - assignment 2","37.89. Parameter play","37.60. Pumpkin varieties and color","37.53. Regression tools","37.44. Regression with Scikit-learn","37.59. Retrying some regression","37.86. Study the solvers","37.56. Try a different model","37.8. Python programming advanced","37.7. Python programming basics","37.6. Python programming introduction","37.5. Project Plan\u200b Template","37.3. First assignment","37.4. Second assignment","8. Data Science in the cloud","8.1. Introduction","8.3. 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Machine Learning overview","11.3. Linear and polynomial regression","11.4. Logistic regression","11.2. Managing data","11. Regression models for Machine Learning","11.1. Tools of the trade","3. Python programming advanced","2. Python programming basics","1. Python programming introduction","38.10. Data Science in real world","38.9. Data Science in the cloud","38.4. Data Science introduction","38.8. Data Science lifecycle","38.7. Data visualization","38.6. NumPy and Pandas","38.5. Relational vs. non-relational database","38.20. Convolutional Neural Network","38.21. Generative Adversarial Network","38. Slides","38.18. Kernel method","38.19. Model Selection","38.17. Unsupervised learning","38.16. Build an machine learning web application","38.12. Linear Regression","38.13. Logistic Regression","38.14. Logistic Regression","38.11. Machine Learning overview","38.15. Neural Network","38.3. Python programming advanced","38.2. Python programming basics","38.1. 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zYXX~5Esd%1J}t6%z(sZgrwdxpc|#;rESv|SF&txVLjZBw4ibC!kv3uhvwn2YLkSDD z)R)|s6Y%A)69G~P7p;&*G>o-^wkEO|(&QT7iX&gzbFL&i@@$ec8uqyUU&fqT+Wx;& zdRK*`1w`khL6ElwU@*uzZQ2@1qznXGMIoG4@l_C3R_3GbL`438=!vlX{$;zu-=N`> k;*7i<9{;cNPWz`=c|U}V4C(p+9)m(@sq3k|P_c{rANd$r#Q*>R diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb index 46a090c45e..6f26d0dc60 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": "375b52a9", + "id": "0cde13d3", "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 f0d003eb32..664ed0876b 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": "4073789d", + "id": "3ff76a87", "metadata": {}, "source": [ "# Exploring visualizations\n", diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb index f9901629d1..3b5290ba90 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": "c4f4e7c1", + "id": "deb5ac91", "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 69b059d9ad..998d8db29f 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": "be4b65ba", + "id": "7c72dcda", "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 da1cb3d3f6..44e72e073a 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": "46278fc3", + "id": "626b8600", "metadata": {}, "source": [ "# Retrying some regression\n", diff --git a/_sources/data-science/data-science-in-the-wild.ipynb b/_sources/data-science/data-science-in-the-wild.ipynb index 2b44b0528b..187acb98d7 100644 --- a/_sources/data-science/data-science-in-the-wild.ipynb +++ b/_sources/data-science/data-science-in-the-wild.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c32a0c5d", + "id": "6ba22aca", "metadata": {}, "source": [ "# Data Science in the real world\n", diff --git a/_sources/data-science/data-science-lifecycle/analyzing.ipynb b/_sources/data-science/data-science-lifecycle/analyzing.ipynb index 59c15936b5..e594d62f24 100644 --- a/_sources/data-science/data-science-lifecycle/analyzing.ipynb +++ b/_sources/data-science/data-science-lifecycle/analyzing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d0d48596", + "id": "f15b1198", "metadata": {}, "source": [ "# Analyzing\n", diff --git a/_sources/data-science/data-science-lifecycle/communication.ipynb b/_sources/data-science/data-science-lifecycle/communication.ipynb index b888232a70..3c94800cbc 100644 --- a/_sources/data-science/data-science-lifecycle/communication.ipynb +++ b/_sources/data-science/data-science-lifecycle/communication.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b5a78097", + "id": "e2167336", "metadata": {}, "source": [ "# Communication\n", diff --git a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb index fac1fc5c10..da642eff6c 100644 --- a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb +++ b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6b16932a", + "id": "2eaaee21", "metadata": {}, "source": [ "# Data Science lifecycle\n", diff --git a/_sources/data-science/data-science-lifecycle/introduction.ipynb b/_sources/data-science/data-science-lifecycle/introduction.ipynb index 182a9afdaf..9b9499619d 100644 --- a/_sources/data-science/data-science-lifecycle/introduction.ipynb +++ b/_sources/data-science/data-science-lifecycle/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "fedb6c3e", + "id": "97a34d02", "metadata": {}, "source": [ "# Introduction to the Data Science lifecycle\n", diff --git a/_sources/data-science/working-with-data/data-preparation.ipynb b/_sources/data-science/working-with-data/data-preparation.ipynb index 1d7c650a0b..11826b35c8 100644 --- a/_sources/data-science/working-with-data/data-preparation.ipynb +++ b/_sources/data-science/working-with-data/data-preparation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "21daa8ac", + "id": "5ff6f8bb", "metadata": {}, "source": [ "# Data preparation\n", @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "16500be0", + "id": "ead36f99", "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "markdown", - "id": "64b3fc70", + "id": "926e750b", "metadata": {}, "source": [ "| |sepal length (cm)|sepal width (cm)|petal length (cm)|petal width (cm)|\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "6b41c90b", + "id": "78b29507", "metadata": {}, "outputs": [ { @@ -93,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "543fc221", + "id": "2c2eb215", "metadata": {}, "source": [ "From this, we know that the *Iris* dataset has 150 entries in four columns with no null entries. All of the data is stored as 64-bit floating-point numbers.\n", @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "b87fe8e4", + "id": "9f788b3b", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "aedb888e", + "id": "bac58214", "metadata": {}, "source": [ "**`DataFrame.tail()`**: Conversely, to check the last few rows of the `DataFrame`, we use the `tail()` method:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "71fca0c9", + "id": "f5603808", "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ }, { "cell_type": "markdown", - "id": "2e2c75e8", + "id": "296536e6", "metadata": {}, "source": [ "```{note}\n", @@ -320,7 +320,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "81fb278e", + "id": "cbd90071", "metadata": {}, "outputs": [ { @@ -347,7 +347,7 @@ }, { "cell_type": "markdown", - "id": "f08ff137", + "id": "3e7d3bd0", "metadata": {}, "source": [ "Look closely at the output. Does any of it surprise you? While `0` is an arithmetic null, it's nevertheless a perfectly good integer and pandas treats it as such. `''` is a little more subtle. While we used it in Section 1 to represent an empty string value, it is nevertheless a string object and not a representation of null as far as pandas is concerned.\n", @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8361e2db", + "id": "77b3ea89", "metadata": {}, "outputs": [ { @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ab1e1bab", + "id": "86f73731", "metadata": {}, "source": [ "Note that this should look like your output from `example3[example3.notnull()]`. The difference here is that, rather than just indexing on the masked values, `dropna` has removed those missing values from the `Series` `example1`.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "d11e19d7", + "id": "d098a164", "metadata": {}, "outputs": [ { @@ -471,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "37e2f592", + "id": "521e0c3b", "metadata": {}, "source": [ "| | 0 | 1 | 2 |\n", @@ -488,7 +488,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "5c6a37d8", + "id": "fe7a6a80", "metadata": {}, "outputs": [ { @@ -544,7 +544,7 @@ }, { "cell_type": "markdown", - "id": "3969ef65", + "id": "d0ebe142", "metadata": {}, "source": [ "If necessary, you can drop NA values from columns. Use `axis=1` to do so:" @@ -553,7 +553,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "76785e9e", + "id": "d32dcefe", "metadata": {}, "outputs": [ { @@ -615,7 +615,7 @@ }, { "cell_type": "markdown", - "id": "c8e3ae25", + "id": "b6748a4f", "metadata": {}, "source": [ "Notice that this can drop a lot of data that you might want to keep, particularly in smaller datasets. What if you just want to drop rows or columns that contain several or even just all null values? You specify those setting in `dropna` with the `how` and `thresh` parameters.\n", @@ -626,7 +626,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "d25d8cef", + "id": "caab6b75", "metadata": {}, "outputs": [ { @@ -701,7 +701,7 @@ }, { "cell_type": "markdown", - "id": "2c8b3f9c", + "id": "c1fb3319", "metadata": {}, "source": [ "| |0 |1 |2 |3 |\n", @@ -716,7 +716,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "371c6912", + "id": "c92d3c0c", "metadata": {}, "outputs": [ { @@ -774,7 +774,7 @@ }, { "cell_type": "markdown", - "id": "c4670e87", + "id": "f2e37f30", "metadata": {}, "source": [ "Here, the first and last rows have been dropped, because they contain only two non-null values.\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "88ac8026", + "id": "2fc6a6dd", "metadata": {}, "outputs": [ { @@ -811,7 +811,7 @@ }, { "cell_type": "markdown", - "id": "f60e5bc1", + "id": "a556caeb", "metadata": {}, "source": [ "You can fill all of the null entries with a single value, such as `0`:" @@ -820,7 +820,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "a5fb5460", + "id": "6c755e66", "metadata": {}, "outputs": [ { @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "6aaf28b7", + "id": "08a87208", "metadata": {}, "source": [ "You can **forward-fill** null values, which is to use the last valid value to fill a null:" @@ -854,7 +854,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "15f3cd14", + "id": "d28bb228", "metadata": {}, "outputs": [ { @@ -879,7 +879,7 @@ }, { "cell_type": "markdown", - "id": "f8fb5572", + "id": "08fddaab", "metadata": {}, "source": [ "You can also **back-fill** to propagate the next valid value backward to fill a null:" @@ -888,7 +888,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2a8d2da3", + "id": "07b37321", "metadata": {}, "outputs": [ { @@ -913,7 +913,7 @@ }, { "cell_type": "markdown", - "id": "ce05fef8", + "id": "8943167a", "metadata": {}, "source": [ "As you might guess, this works the same with `DataFrame`s, but you can also specify an `axis` along which to fill null values. taking the previously used `example2` again:" @@ -922,7 +922,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "bf109926", + "id": "b5efa5e5", "metadata": {}, "outputs": [ { @@ -996,7 +996,7 @@ }, { "cell_type": "markdown", - "id": "cd45335d", + "id": "23d05d8a", "metadata": {}, "source": [ "Notice that when a previous value is not available for forward-filling, the null value remains.\n", @@ -1019,7 +1019,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "2e9225db", + "id": "3747dc82", "metadata": {}, "outputs": [ { @@ -1099,7 +1099,7 @@ }, { "cell_type": "markdown", - "id": "d9e30710", + "id": "faeab8ac", "metadata": {}, "source": [ "| |letters|numbers|\n", @@ -1114,7 +1114,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "851d33b1", + "id": "20ee0847", "metadata": {}, "outputs": [ { @@ -1139,7 +1139,7 @@ }, { "cell_type": "markdown", - "id": "e66e3835", + "id": "43cea19c", "metadata": {}, "source": [ "**Dropping duplicates: `drop_duplicates`:** simply returns a copy of the data for which all of the `duplicated` values are `False`:" @@ -1148,7 +1148,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "4f6253a5", + "id": "36b7b407", "metadata": {}, "outputs": [ { @@ -1214,7 +1214,7 @@ }, { "cell_type": "markdown", - "id": "d11a81f5", + "id": "f8077851", "metadata": {}, "source": [ "Both `duplicated` and `drop_duplicates` default to consider all columns but you can specify that they examine only a subset of columns in your `DataFrame`:" @@ -1223,7 +1223,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "e46cee94", + "id": "2f8b31d8", "metadata": {}, "outputs": [ { @@ -1283,7 +1283,7 @@ }, { "cell_type": "markdown", - "id": "28d1210c", + "id": "abbffb73", "metadata": {}, "source": [ "```{note}\n", diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb index 7e3415eb5f..3f2023517b 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": "26df044f", + "id": "32b47750", "metadata": {}, "source": [ "# NumPy\n", @@ -19,7 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "639f7e9c", + "id": "372e7d2d", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "58c3425f", + "id": "727bacc2", "metadata": {}, "source": [ "### Create a basic array\n", @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "67a8279a", + "id": "38e45b31", "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "1eb6110e", + "id": "ed4ca0fc", "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": "9f2e614a", + "id": "131885e3", "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ }, { "cell_type": "markdown", - "id": "33c11942", + "id": "90eae216", "metadata": {}, "source": [ "Or an array filled with 1’s:" @@ -113,7 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "013a4d32", + "id": "bf7b9538", "metadata": {}, "outputs": [ { @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "e06dd41e", + "id": "b4f73d60", "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": "320cff03", + "id": "d06b3f0d", "metadata": {}, "outputs": [ { @@ -162,7 +162,7 @@ }, { "cell_type": "markdown", - "id": "6b88c494", + "id": "5bf953e9", "metadata": {}, "source": [ "You can create an array with a range of elements:" @@ -171,7 +171,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "1a2ed2bd", + "id": "ac8617ca", "metadata": {}, "outputs": [ { @@ -191,7 +191,7 @@ }, { "cell_type": "markdown", - "id": "421ef3f3", + "id": "281d5f5c", "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": "348232bf", + "id": "6b481225", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "2ab9b967", + "id": "bcca2422", "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": "726ba671", + "id": "30c3df71", "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "bb24eef2", + "id": "c9a12bad", "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": "a56da42a", + "id": "df7dc066", "metadata": {}, "outputs": [ { @@ -278,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "3ab57724", + "id": "f37840e6", "metadata": {}, "source": [ "### Adding, removing, and sorting elements\n", @@ -291,7 +291,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "68c60214", + "id": "72fc49f9", "metadata": {}, "outputs": [], "source": [ @@ -300,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "dd36db00", + "id": "b834bc5c", "metadata": {}, "source": [ "You can quickly sort the numbers in ascending order with:" @@ -309,7 +309,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "3b445999", + "id": "1ef9592a", "metadata": {}, "outputs": [ { @@ -329,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "6ae4729f", + "id": "c46fac69", "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": "89e299c5", + "id": "4b0666df", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "3d7be007", + "id": "da7f2e61", "metadata": {}, "source": [ "You can concatenate them with `np.concatenate()`." @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "204b53fe", + "id": "a60f122f", "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "4d2ed678", + "id": "96011104", "metadata": {}, "source": [ "Or, if you start with these arrays:" @@ -393,7 +393,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "27e18841", + "id": "40791a62", "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ }, { "cell_type": "markdown", - "id": "94de63b3", + "id": "c62f3f22", "metadata": {}, "source": [ "You can concatenate them with:" @@ -412,7 +412,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "e2d73dd8", + "id": "4929ab1b", "metadata": {}, "outputs": [ { @@ -434,7 +434,7 @@ }, { "cell_type": "markdown", - "id": "f0732cdf", + "id": "cd0c8dfc", "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": "c4b07961", + "id": "00cefd0a", "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "256397e6", + "id": "2368150b", "metadata": {}, "outputs": [ { @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "dc18dd33", + "id": "5bada056", "metadata": {}, "source": [ "- ndarray.shape\n", @@ -505,7 +505,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "77dc06bc", + "id": "b04f970a", "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "2a12dfb6", + "id": "4d302d41", "metadata": {}, "source": [ "- ndarray.size\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "bf368775", + "id": "977d1250", "metadata": {}, "outputs": [ { @@ -555,7 +555,7 @@ }, { "cell_type": "markdown", - "id": "3d62ef04", + "id": "fb352ae5", "metadata": {}, "source": [ "- ndarray.dtype\n", @@ -565,7 +565,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "f00abe42", + "id": "5b7f91c9", "metadata": {}, "outputs": [ { @@ -586,7 +586,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "b5504fbd", + "id": "b1ee18d2", "metadata": {}, "outputs": [ { @@ -606,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "ad5f7ff9", + "id": "f0d411da", "metadata": {}, "source": [ "- ndarray.itemsize\n", @@ -616,7 +616,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "f26d8693", + "id": "9acb3d42", "metadata": {}, "outputs": [ { @@ -636,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "5b83c510", + "id": "e4077daa", "metadata": {}, "source": [ "- ndarray.data\n", @@ -646,13 +646,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "e9c1f03b", + "id": "64ca190e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -666,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "0ba8edc5", + "id": "70b2df02", "metadata": {}, "source": [ "### Reshape an array\n", @@ -679,7 +679,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "652d07a5", + "id": "8c3e0c42", "metadata": {}, "outputs": [ { @@ -700,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "757b63fc", + "id": "af431acd", "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": "a157fc71", + "id": "8b1c739d", "metadata": {}, "outputs": [ { @@ -732,7 +732,7 @@ }, { "cell_type": "markdown", - "id": "475d7643", + "id": "a11a600e", "metadata": {}, "source": [ "With `np.reshape`, you can specify a few optional parameters:" @@ -741,7 +741,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "cbe5f4e1", + "id": "448f4a75", "metadata": {}, "outputs": [ { @@ -761,7 +761,7 @@ }, { "cell_type": "markdown", - "id": "a118cd19", + "id": "b9c0ce78", "metadata": {}, "source": [ "`a` is the array to be reshaped.\n", @@ -782,7 +782,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "f34cf4c0", + "id": "572e5cd0", "metadata": {}, "outputs": [ { @@ -803,7 +803,7 @@ }, { "cell_type": "markdown", - "id": "dc77089a", + "id": "d175cc5e", "metadata": {}, "source": [ "You can use `np.newaxis` to add a new axis:" @@ -812,7 +812,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "61c3e030", + "id": "763ac7c3", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "5a32c35b", + "id": "a108230a", "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": "ce884611", + "id": "39cdb8d2", "metadata": {}, "outputs": [ { @@ -863,7 +863,7 @@ }, { "cell_type": "markdown", - "id": "e97dd5b8", + "id": "e3033390", "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": "43528ccc", + "id": "30332c9e", "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ }, { "cell_type": "markdown", - "id": "7f51fd26", + "id": "9edd01e0", "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": "0cadc802", + "id": "a2d11b32", "metadata": {}, "outputs": [ { @@ -925,7 +925,7 @@ }, { "cell_type": "markdown", - "id": "a500dcf6", + "id": "a4baffb8", "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": "b38d7e48", + "id": "2862ad84", "metadata": {}, "outputs": [ { @@ -955,7 +955,7 @@ }, { "cell_type": "markdown", - "id": "8f724e1d", + "id": "6bbc0083", "metadata": {}, "source": [ "You can add an axis at index position 0 with:" @@ -964,7 +964,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "76687b8f", + "id": "6a395235", "metadata": {}, "outputs": [ { @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "f82ab1ce", + "id": "dc213ed7", "metadata": {}, "source": [ "### Indexing and slicing\n", @@ -996,7 +996,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "f319d9ae", + "id": "4444daf5", "metadata": {}, "outputs": [], "source": [ @@ -1006,7 +1006,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "3e1b50c8", + "id": "95936c8f", "metadata": {}, "outputs": [ { @@ -1027,7 +1027,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "0eff095e", + "id": "abb6d7c1", "metadata": {}, "outputs": [ { @@ -1048,7 +1048,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "e83b0b8e", + "id": "17d94f19", "metadata": {}, "outputs": [ { @@ -1069,7 +1069,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "e7cee670", + "id": "91e00a19", "metadata": {}, "outputs": [ { @@ -1089,7 +1089,7 @@ }, { "cell_type": "markdown", - "id": "1da34809", + "id": "87fc69ff", "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": "bb1892ab", + "id": "a4515856", "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1111,7 @@ }, { "cell_type": "markdown", - "id": "beb77fe9", + "id": "d7d67364", "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": "7e705a02", + "id": "932cee50", "metadata": {}, "outputs": [ { @@ -1140,7 +1140,7 @@ }, { "cell_type": "markdown", - "id": "18704444", + "id": "2cbfa762", "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": "6d575ea6", + "id": "2c98c76b", "metadata": {}, "outputs": [ { @@ -1170,7 +1170,7 @@ }, { "cell_type": "markdown", - "id": "9b58de66", + "id": "c56553fa", "metadata": {}, "source": [ "You can select elements that are divisible by 2:" @@ -1179,7 +1179,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "26718b28", + "id": "1cca7d49", "metadata": {}, "outputs": [ { @@ -1200,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "66545416", + "id": "e911a3ee", "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": "0ecca13b", + "id": "d604205e", "metadata": {}, "outputs": [ { @@ -1230,7 +1230,7 @@ }, { "cell_type": "markdown", - "id": "a208682c", + "id": "270e485b", "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": "d485f8cb", + "id": "2bbcef14", "metadata": {}, "outputs": [ { @@ -1262,7 +1262,7 @@ }, { "cell_type": "markdown", - "id": "183145c5", + "id": "79a8ac28", "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": "e3bc0fce", + "id": "6bc0f8c3", "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "720f93cd", + "id": "3ec400a2", "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": "9aa61cae", + "id": "4c9dc72d", "metadata": {}, "outputs": [ { @@ -1312,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "53405376", + "id": "32664d51", "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": "31099d2e", + "id": "bfe01ec5", "metadata": {}, "outputs": [ { @@ -1345,7 +1345,7 @@ }, { "cell_type": "markdown", - "id": "514076ac", + "id": "9506b05b", "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": "802d7f0e", + "id": "d86996e0", "metadata": {}, "outputs": [ { @@ -1374,7 +1374,7 @@ }, { "cell_type": "markdown", - "id": "7d7ab3f2", + "id": "a5a94fb7", "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": "00a394e1", + "id": "142b4822", "metadata": {}, "outputs": [ { @@ -1404,7 +1404,7 @@ }, { "cell_type": "markdown", - "id": "72131317", + "id": "ed63ad9b", "metadata": {}, "source": [ "### Create an array from existing data\n", @@ -1417,7 +1417,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "fe21870f", + "id": "e4763a20", "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "62e2f1fe", + "id": "e7e94ae3", "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": "feb14035", + "id": "7dac8962", "metadata": {}, "outputs": [ { @@ -1456,7 +1456,7 @@ }, { "cell_type": "markdown", - "id": "e7ecf728", + "id": "0ca7d1df", "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": "6f3e926d", + "id": "c13b1a53", "metadata": {}, "outputs": [], "source": [ @@ -1479,7 +1479,7 @@ }, { "cell_type": "markdown", - "id": "fb7c6e4f", + "id": "496b3d71", "metadata": {}, "source": [ "You can stack them vertically with `vstack`:" @@ -1488,7 +1488,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "937ec97f", + "id": "2ef72823", "metadata": {}, "outputs": [ { @@ -1511,7 +1511,7 @@ }, { "cell_type": "markdown", - "id": "89d736b6", + "id": "9f86a4e7", "metadata": {}, "source": [ "Or stack them horizontally with hstack:" @@ -1520,7 +1520,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "f7150024", + "id": "59658802", "metadata": {}, "outputs": [ { @@ -1541,7 +1541,7 @@ }, { "cell_type": "markdown", - "id": "e6205d9d", + "id": "e747a25d", "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": "c29fe1cb", + "id": "766f8e94", "metadata": {}, "outputs": [ { @@ -1574,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "a3633f0b", + "id": "d8a3d52a", "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": "4dc14ead", + "id": "c5c7d58b", "metadata": {}, "outputs": [ { @@ -1608,7 +1608,7 @@ }, { "cell_type": "markdown", - "id": "cdcfce2f", + "id": "adfa58fb", "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": "a015db75", + "id": "4b63dde4", "metadata": {}, "outputs": [ { @@ -1642,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "3c353310", + "id": "cd8ae50e", "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": "3907e2f0", + "id": "07fff1f2", "metadata": {}, "outputs": [], "source": [ @@ -1664,7 +1664,7 @@ }, { "cell_type": "markdown", - "id": "66acf107", + "id": "736ef12c", "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": "fa17b307", + "id": "a20c4dff", "metadata": {}, "outputs": [ { @@ -1695,7 +1695,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "cb6e8bbf", + "id": "891e54a2", "metadata": {}, "outputs": [ { @@ -1717,7 +1717,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "3bd99881", + "id": "60fac7a6", "metadata": {}, "outputs": [ { @@ -1739,7 +1739,7 @@ }, { "cell_type": "markdown", - "id": "29b22c76", + "id": "707f7b00", "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": "8aa9494f", + "id": "bee94541", "metadata": {}, "outputs": [], "source": [ @@ -1757,7 +1757,7 @@ }, { "cell_type": "markdown", - "id": "98b56cf8", + "id": "b3be3f24", "metadata": {}, "source": [ "## Array operations\n", @@ -1770,7 +1770,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "5d271f03", + "id": "fecfe8e5", "metadata": {}, "outputs": [], "source": [ @@ -1780,7 +1780,7 @@ }, { "cell_type": "markdown", - "id": "d394adbb", + "id": "a5d6ae1a", "metadata": {}, "source": [ "You can add the arrays together with the plus sign." @@ -1789,7 +1789,7 @@ { "cell_type": "code", "execution_count": 64, - "id": "8589a275", + "id": "0cb65ad5", "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "0258b325", + "id": "885324c7", "metadata": {}, "source": [ "You can, of course, do more than just addition!" @@ -1818,7 +1818,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "e215cc4c", + "id": "59cad567", "metadata": {}, "outputs": [ { @@ -1839,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "2cd070df", + "id": "1a69c958", "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": "e2b98e6b", + "id": "8cb47987", "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ }, { "cell_type": "markdown", - "id": "e9e4b2d1", + "id": "46300a34", "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": "1f6517bb", + "id": "bf45bfcb", "metadata": {}, "outputs": [], "source": [ @@ -1889,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "9a0afdb4", + "id": "2a33841c", "metadata": {}, "source": [ "You can sum over the axis of rows with:" @@ -1898,7 +1898,7 @@ { "cell_type": "code", "execution_count": 68, - "id": "9ebfebfb", + "id": "625dbe3b", "metadata": {}, "outputs": [ { @@ -1918,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "48d3cf45", + "id": "726bcc80", "metadata": {}, "source": [ "You can sum over the axis of columns with:" @@ -1927,7 +1927,7 @@ { "cell_type": "code", "execution_count": 69, - "id": "c03d1d11", + "id": "ae128609", "metadata": {}, "outputs": [ { @@ -1947,7 +1947,7 @@ }, { "cell_type": "markdown", - "id": "da66d9dc", + "id": "0b811e63", "metadata": {}, "source": [ "### Universal functions(ufunc)\n", @@ -2038,7 +2038,7 @@ { "cell_type": "code", "execution_count": 70, - "id": "9c5ec5fb", + "id": "611e7343", "metadata": {}, "outputs": [ { @@ -2060,7 +2060,7 @@ }, { "cell_type": "markdown", - "id": "e4650d70", + "id": "3a017d73", "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": "69d37f65", + "id": "bb76bbc9", "metadata": {}, "outputs": [ { @@ -2091,7 +2091,7 @@ }, { "cell_type": "markdown", - "id": "363ade7d", + "id": "57ce6f5b", "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": "d42ca203", + "id": "97e26eb0", "metadata": {}, "outputs": [ { @@ -2154,7 +2154,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "17730997", + "id": "2af8e826", "metadata": {}, "outputs": [ { @@ -2175,7 +2175,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "118b3a70", + "id": "72f37397", "metadata": {}, "outputs": [ { @@ -2195,7 +2195,7 @@ }, { "cell_type": "markdown", - "id": "0cc1a266", + "id": "546f2bde", "metadata": {}, "source": [ "Let’s start with this array, called “a”." @@ -2204,7 +2204,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "f5ff443c", + "id": "afe7ed81", "metadata": {}, "outputs": [], "source": [ @@ -2215,7 +2215,7 @@ }, { "cell_type": "markdown", - "id": "9853e0d5", + "id": "6fb6b29e", "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": "c6f4ca78", + "id": "f3771a4d", "metadata": {}, "outputs": [ { @@ -2244,7 +2244,7 @@ }, { "cell_type": "markdown", - "id": "ce7ec19c", + "id": "9d65f7eb", "metadata": {}, "source": [ "Or:" @@ -2253,7 +2253,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "de936b74", + "id": "c7ea36ea", "metadata": {}, "outputs": [ { @@ -2273,7 +2273,7 @@ }, { "cell_type": "markdown", - "id": "07b818fa", + "id": "db42b8ce", "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": "a9919b51", + "id": "394bb709", "metadata": {}, "outputs": [ { @@ -2302,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "fdb24859", + "id": "546f573a", "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": "891f20eb", + "id": "1e6b10f5", "metadata": {}, "outputs": [], "source": [ @@ -2335,7 +2335,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "430f370c", + "id": "87fea887", "metadata": {}, "outputs": [ { @@ -2356,7 +2356,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "c8f13aaa", + "id": "23b85bb0", "metadata": {}, "outputs": [ { @@ -2376,7 +2376,7 @@ }, { "cell_type": "markdown", - "id": "78b23e98", + "id": "03e34adf", "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": "5c473d42", + "id": "44a58a76", "metadata": {}, "outputs": [], "source": [ @@ -2395,7 +2395,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "c2371626", + "id": "0b003a7f", "metadata": {}, "outputs": [ { @@ -2416,7 +2416,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "8440c8d4", + "id": "2ebf966e", "metadata": {}, "outputs": [ { @@ -2436,7 +2436,7 @@ }, { "cell_type": "markdown", - "id": "cbd77d4e", + "id": "acf49d88", "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": "43465e15", + "id": "57585424", "metadata": {}, "outputs": [ { @@ -2465,7 +2465,7 @@ }, { "cell_type": "markdown", - "id": "a66843c9", + "id": "ebccda39", "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": "bd21f5c4", + "id": "1b459d08", "metadata": {}, "outputs": [ { @@ -2494,7 +2494,7 @@ }, { "cell_type": "markdown", - "id": "c0c7ccde", + "id": "646ddeb4", "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": "07bd56ef", + "id": "a4536d77", "metadata": {}, "outputs": [ { @@ -2542,7 +2542,7 @@ }, { "cell_type": "markdown", - "id": "fcfac40f", + "id": "a516a7eb", "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": "048b59f4", + "id": "e4baf9f6", "metadata": {}, "outputs": [ { @@ -2572,7 +2572,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "84757c4d", + "id": "65971161", "metadata": {}, "outputs": [ { @@ -2592,7 +2592,7 @@ }, { "cell_type": "markdown", - "id": "3b2fdb43", + "id": "5443a00b", "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": "30695935", + "id": "974188b9", "metadata": {}, "outputs": [ { @@ -2621,7 +2621,7 @@ }, { "cell_type": "markdown", - "id": "330bd5ac", + "id": "aec85638", "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": "9838bfd2", + "id": "44518e40", "metadata": {}, "outputs": [ { @@ -2652,7 +2652,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "0e244e06", + "id": "82b0da4f", "metadata": {}, "outputs": [ { @@ -2674,7 +2674,7 @@ }, { "cell_type": "markdown", - "id": "4d559829", + "id": "496c4c19", "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": "9a50f69f", + "id": "7018a8c1", "metadata": {}, "outputs": [ { @@ -2720,7 +2720,7 @@ }, { "cell_type": "markdown", - "id": "3168d462", + "id": "cff695b5", "metadata": {}, "source": [ "This is equivalent to:" @@ -2729,7 +2729,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "c171f937", + "id": "ec9f76b6", "metadata": {}, "outputs": [ { @@ -2750,7 +2750,7 @@ }, { "cell_type": "markdown", - "id": "f10eca8b", + "id": "e3a1c9fe", "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": "e45841c7", + "id": "5b9f83c1", "metadata": {}, "outputs": [ { @@ -2780,7 +2780,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "eef9df8e", + "id": "c337fa99", "metadata": {}, "outputs": [ { @@ -2800,7 +2800,7 @@ }, { "cell_type": "markdown", - "id": "745aba87", + "id": "c8b7a92b", "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": "62848397", + "id": "7d92d62b", "metadata": {}, "outputs": [ { @@ -2834,7 +2834,7 @@ }, { "cell_type": "markdown", - "id": "f86ca8b2", + "id": "fce5b76b", "metadata": {}, "source": [ "### Advanced indexing\n", @@ -2857,7 +2857,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "40894d5e", + "id": "216ff1d7", "metadata": {}, "outputs": [], "source": [ @@ -2867,7 +2867,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "e23dde7a", + "id": "301058dd", "metadata": {}, "outputs": [ { @@ -2888,7 +2888,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "a78aed57", + "id": "6d96ecfc", "metadata": {}, "outputs": [ { @@ -2909,7 +2909,7 @@ { "cell_type": "code", "execution_count": 101, - "id": "f17ca511", + "id": "db8694d4", "metadata": {}, "outputs": [ { @@ -2929,7 +2929,7 @@ }, { "cell_type": "markdown", - "id": "8802458d", + "id": "d3a744d1", "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": "ad1e9a5a", + "id": "e9ac7d02", "metadata": {}, "outputs": [], "source": [ @@ -2948,7 +2948,7 @@ { "cell_type": "code", "execution_count": 103, - "id": "e6cb7d13", + "id": "7ba59cdc", "metadata": {}, "outputs": [ { @@ -2969,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "e234e871", + "id": "fb260c3a", "metadata": {}, "source": [ "```py\n", @@ -2999,7 +2999,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "9fdb98e0", + "id": "6be03ba8", "metadata": {}, "outputs": [], "source": [ @@ -3009,7 +3009,7 @@ { "cell_type": "code", "execution_count": 105, - "id": "43d82062", + "id": "bdc352ea", "metadata": {}, "outputs": [ { @@ -3034,7 +3034,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "f0a2dfa4", + "id": "b669b21e", "metadata": {}, "outputs": [ { @@ -3054,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "aab0d978", + "id": "25221a26", "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": "715e99c5", + "id": "522085cd", "metadata": {}, "outputs": [ { @@ -3097,7 +3097,7 @@ }, { "cell_type": "markdown", - "id": "d63b4484", + "id": "470d2364", "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": "8a16e9f8", + "id": "95c4e721", "metadata": {}, "outputs": [ { @@ -3128,7 +3128,7 @@ }, { "cell_type": "markdown", - "id": "66b89963", + "id": "21552874", "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": "b5946079", + "id": "cdd0f6dd", "metadata": {}, "outputs": [ { @@ -3164,7 +3164,7 @@ }, { "cell_type": "markdown", - "id": "39398bc5", + "id": "ba6089d3", "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": "98399336", + "id": "3c40b84d", "metadata": {}, "outputs": [ { @@ -3206,7 +3206,7 @@ }, { "cell_type": "markdown", - "id": "32a705f6", + "id": "f3f1ebea", "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": "bd6be710", + "id": "ee98e633", "metadata": {}, "outputs": [], "source": [ @@ -3226,7 +3226,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "e805c484", + "id": "bb7ed708", "metadata": {}, "outputs": [ { @@ -3248,7 +3248,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "02b07429", + "id": "cca931d2", "metadata": {}, "outputs": [ { @@ -3269,7 +3269,7 @@ }, { "cell_type": "markdown", - "id": "ca461196", + "id": "2bff7779", "metadata": {}, "source": [ "This broadcasting can also be achieved using the function `ix_`:" @@ -3278,7 +3278,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "5ed1a21e", + "id": "6b57d4a9", "metadata": {}, "outputs": [ { @@ -3299,7 +3299,7 @@ }, { "cell_type": "markdown", - "id": "9c5e611f", + "id": "bb9db1e9", "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": "bf36f567", + "id": "7be0d041", "metadata": {}, "outputs": [ { @@ -3328,7 +3328,7 @@ }, { "cell_type": "markdown", - "id": "ed63cbac", + "id": "2714d4f5", "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": "32c43872", + "id": "510e40cd", "metadata": {}, "outputs": [ { @@ -3370,7 +3370,7 @@ }, { "cell_type": "markdown", - "id": "34831f87", + "id": "c8c365fc", "metadata": {}, "source": [ "Or wish to add a constant to all negative elements:" @@ -3379,7 +3379,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "111aa78d", + "id": "04ed1fbe", "metadata": {}, "outputs": [ { @@ -3401,7 +3401,7 @@ }, { "cell_type": "markdown", - "id": "b4755dfa", + "id": "a485da5a", "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": "676a24a0", + "id": "ded1cf79", "metadata": {}, "outputs": [], "source": [ @@ -3425,7 +3425,7 @@ { "cell_type": "code", "execution_count": 119, - "id": "9342d31e", + "id": "84d72d24", "metadata": {}, "outputs": [ { @@ -3446,7 +3446,7 @@ { "cell_type": "code", "execution_count": 120, - "id": "d280361a", + "id": "ee053890", "metadata": {}, "outputs": [ { @@ -3467,7 +3467,7 @@ }, { "cell_type": "markdown", - "id": "50ea3c69", + "id": "c44d4ffb", "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": "68f60c9e", + "id": "51eda7ce", "metadata": {}, "outputs": [ { @@ -3503,7 +3503,7 @@ }, { "cell_type": "markdown", - "id": "b0642557", + "id": "41d05686", "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": "0d307621", + "id": "513322ba", "metadata": {}, "outputs": [], "source": [ @@ -3530,7 +3530,7 @@ { "cell_type": "code", "execution_count": 123, - "id": "bc3aa448", + "id": "951bb9a6", "metadata": {}, "outputs": [ { @@ -3551,7 +3551,7 @@ { "cell_type": "code", "execution_count": 124, - "id": "bdb37b53", + "id": "72a1a23b", "metadata": {}, "outputs": [], "source": [ @@ -3561,7 +3561,7 @@ { "cell_type": "code", "execution_count": 125, - "id": "5806efee", + "id": "ad4fa8b6", "metadata": {}, "outputs": [ { @@ -3582,7 +3582,7 @@ }, { "cell_type": "markdown", - "id": "444524a9", + "id": "bc428762", "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": "9c49d837", + "id": "e548612b", "metadata": {}, "outputs": [ { @@ -3615,7 +3615,7 @@ }, { "cell_type": "markdown", - "id": "09ef8785", + "id": "6e27e116", "metadata": {}, "source": [ "##### Example 3\n", @@ -3626,7 +3626,7 @@ { "cell_type": "code", "execution_count": 127, - "id": "3ea8713e", + "id": "a0634f12", "metadata": {}, "outputs": [ { @@ -3654,7 +3654,7 @@ { "cell_type": "code", "execution_count": 128, - "id": "6dfc825d", + "id": "b98e823e", "metadata": {}, "outputs": [ { @@ -3678,7 +3678,7 @@ }, { "cell_type": "markdown", - "id": "e19fe24c", + "id": "ab6cdcf9", "metadata": {}, "source": [ "#### Combining advanced and basic indexing\n", @@ -3691,7 +3691,7 @@ { "cell_type": "code", "execution_count": 129, - "id": "e4e47f80", + "id": "4c55d977", "metadata": {}, "outputs": [ { @@ -3714,7 +3714,7 @@ }, { "cell_type": "markdown", - "id": "5251af62", + "id": "73f0a928", "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": "5a59149e", + "id": "d111adf6", "metadata": {}, "outputs": [ { @@ -3745,7 +3745,7 @@ }, { "cell_type": "markdown", - "id": "e6cb1904", + "id": "f4d1b821", "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": "b30046a1", + "id": "418197af", "metadata": {}, "outputs": [], "source": [ @@ -3767,7 +3767,7 @@ { "cell_type": "code", "execution_count": 132, - "id": "96db8003", + "id": "8166c375", "metadata": {}, "outputs": [ { @@ -3788,7 +3788,7 @@ { "cell_type": "code", "execution_count": 133, - "id": "f808257c", + "id": "aa468e5b", "metadata": {}, "outputs": [ { @@ -3808,7 +3808,7 @@ }, { "cell_type": "markdown", - "id": "dc1581b2", + "id": "93d8a891", "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": "d32641f8", + "id": "736a486e", "metadata": {}, "outputs": [], "source": [ @@ -3845,7 +3845,7 @@ { "cell_type": "code", "execution_count": 135, - "id": "5f1e8a7f", + "id": "2950edd5", "metadata": {}, "outputs": [ { @@ -3870,7 +3870,7 @@ { "cell_type": "code", "execution_count": 136, - "id": "ec68edc1", + "id": "e629d044", "metadata": {}, "outputs": [ { @@ -3891,7 +3891,7 @@ }, { "cell_type": "markdown", - "id": "a39ed51a", + "id": "6f9e517e", "metadata": {}, "source": [ "### Field access\n", @@ -3908,7 +3908,7 @@ { "cell_type": "code", "execution_count": 137, - "id": "1411547e", + "id": "eccedbcb", "metadata": {}, "outputs": [], "source": [ @@ -3918,7 +3918,7 @@ { "cell_type": "code", "execution_count": 138, - "id": "cdecac44", + "id": "0dc464f2", "metadata": {}, "outputs": [ { @@ -3939,7 +3939,7 @@ { "cell_type": "code", "execution_count": 139, - "id": "7418c946", + "id": "22898632", "metadata": {}, "outputs": [ { @@ -3960,7 +3960,7 @@ { "cell_type": "code", "execution_count": 140, - "id": "45aa035e", + "id": "a4390a58", "metadata": {}, "outputs": [ { @@ -3981,7 +3981,7 @@ { "cell_type": "code", "execution_count": 141, - "id": "2c608a95", + "id": "c9170509", "metadata": {}, "outputs": [ { @@ -4001,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "afee0e0c", + "id": "562f171b", "metadata": {}, "source": [ "### Flat Iterator indexing\n", @@ -4016,7 +4016,7 @@ { "cell_type": "code", "execution_count": 142, - "id": "742df6d4", + "id": "c2f8241d", "metadata": {}, "outputs": [], "source": [ @@ -4026,7 +4026,7 @@ }, { "cell_type": "markdown", - "id": "5b426150", + "id": "9174564b", "metadata": {}, "source": [ "Or an array of the right size:" @@ -4035,7 +4035,7 @@ { "cell_type": "code", "execution_count": 143, - "id": "137fb0fd", + "id": "d9fa2290", "metadata": {}, "outputs": [], "source": [ @@ -4044,7 +4044,7 @@ }, { "cell_type": "markdown", - "id": "50845ef8", + "id": "2fafcb75", "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": "0447411c", + "id": "734a90c1", "metadata": {}, "outputs": [ { @@ -4074,7 +4074,7 @@ }, { "cell_type": "markdown", - "id": "e03449ca", + "id": "bb5a3100", "metadata": {}, "source": [ "```py\n", @@ -4093,7 +4093,7 @@ { "cell_type": "code", "execution_count": 145, - "id": "4c3fc47b", + "id": "06aad338", "metadata": {}, "outputs": [ { @@ -4115,7 +4115,7 @@ { "cell_type": "code", "execution_count": 146, - "id": "7d7f9edc", + "id": "001564da", "metadata": {}, "outputs": [ { @@ -4136,7 +4136,7 @@ }, { "cell_type": "markdown", - "id": "778839a8", + "id": "9bf53a8e", "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": "a5f016a2", + "id": "4f5d811e", "metadata": {}, "outputs": [ { @@ -4171,7 +4171,7 @@ }, { "cell_type": "markdown", - "id": "15de32de", + "id": "5517f3d7", "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": "e1bd432a", + "id": "5c82da27", "metadata": {}, "outputs": [ { @@ -4203,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "78e5454c", + "id": "796d983a", "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": "c153c815", + "id": "b51cd8dd", "metadata": {}, "outputs": [ { @@ -4235,7 +4235,7 @@ }, { "cell_type": "markdown", - "id": "3a79c78b", + "id": "f562da03", "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": "709c07b2", + "id": "dd253d89", "metadata": {}, "outputs": [ { @@ -4316,7 +4316,7 @@ { "cell_type": "code", "execution_count": 151, - "id": "486b3c0c", + "id": "43dbbffd", "metadata": {}, "outputs": [ { @@ -4336,7 +4336,7 @@ }, { "cell_type": "markdown", - "id": "cd9fb5e8", + "id": "0aed2b90", "metadata": {}, "source": [ "## Structured arrays\n", @@ -4349,7 +4349,7 @@ { "cell_type": "code", "execution_count": 152, - "id": "55995518", + "id": "75b75c82", "metadata": {}, "outputs": [ { @@ -4372,7 +4372,7 @@ }, { "cell_type": "markdown", - "id": "59e7d7ce", + "id": "2be93ab2", "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": "dc7d3bec", + "id": "13e19ccc", "metadata": {}, "outputs": [ { @@ -4403,7 +4403,7 @@ }, { "cell_type": "markdown", - "id": "b1549776", + "id": "da6a3ef6", "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": "79707978", + "id": "b90c2d13", "metadata": {}, "outputs": [ { @@ -4433,7 +4433,7 @@ { "cell_type": "code", "execution_count": 155, - "id": "62e8ddc0", + "id": "f04b35cb", "metadata": {}, "outputs": [ { @@ -4454,7 +4454,7 @@ { "cell_type": "code", "execution_count": 156, - "id": "dde264d9", + "id": "6a9d9f4e", "metadata": {}, "outputs": [ { @@ -4475,7 +4475,7 @@ }, { "cell_type": "markdown", - "id": "21fcf439", + "id": "1c188a75", "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": "bb25d3fb", + "id": "7491891c", "metadata": {}, "outputs": [ { @@ -4518,7 +4518,7 @@ }, { "cell_type": "markdown", - "id": "99121b0c", + "id": "47685f10", "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": "fa2b7b4a", + "id": "b17a4479", "metadata": {}, "outputs": [ { @@ -4547,7 +4547,7 @@ }, { "cell_type": "markdown", - "id": "938fbce7", + "id": "050c21d3", "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": "b90c587c", + "id": "9a2035fb", "metadata": {}, "outputs": [ { @@ -4581,7 +4581,7 @@ { "cell_type": "code", "execution_count": 160, - "id": "fe1304fe", + "id": "1c0d118e", "metadata": {}, "outputs": [ { @@ -4601,7 +4601,7 @@ }, { "cell_type": "markdown", - "id": "fac5bb80", + "id": "30b85d2a", "metadata": {}, "source": [ "- A dictionary of field parameter arrays\n", @@ -4614,7 +4614,7 @@ { "cell_type": "code", "execution_count": 161, - "id": "d1e31afb", + "id": "a20dbbdc", "metadata": {}, "outputs": [ { @@ -4635,7 +4635,7 @@ { "cell_type": "code", "execution_count": 162, - "id": "7a016d54", + "id": "c8610697", "metadata": {}, "outputs": [ { @@ -4658,7 +4658,7 @@ }, { "cell_type": "markdown", - "id": "40cabea2", + "id": "a8d10a39", "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": "99cb1995", + "id": "149ceecc", "metadata": {}, "outputs": [ { @@ -4693,7 +4693,7 @@ }, { "cell_type": "markdown", - "id": "70491a6d", + "id": "62a2a74d", "metadata": {}, "source": [ "#### Manipulating and Displaying Structured Datatypes\n", @@ -4704,7 +4704,7 @@ { "cell_type": "code", "execution_count": 164, - "id": "f02aa357", + "id": "46dfa70b", "metadata": {}, "outputs": [ { @@ -4725,7 +4725,7 @@ }, { "cell_type": "markdown", - "id": "4adbfcfc", + "id": "20b94676", "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": "86ed67ad", + "id": "e405bb67", "metadata": {}, "outputs": [ { @@ -4756,7 +4756,7 @@ }, { "cell_type": "markdown", - "id": "7963c01d", + "id": "40b4a3d9", "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": "d410a8a3", + "id": "f2ad6f54", "metadata": {}, "outputs": [ { @@ -4793,7 +4793,7 @@ }, { "cell_type": "markdown", - "id": "a196d312", + "id": "a06644f2", "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": "bce8a60a", + "id": "ad6fb0db", "metadata": {}, "outputs": [ { @@ -4822,7 +4822,7 @@ }, { "cell_type": "markdown", - "id": "d67ced8c", + "id": "01c41efc", "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": "a837c3cf", + "id": "6ebd7920", "metadata": {}, "outputs": [ { @@ -4850,7 +4850,7 @@ }, { "cell_type": "markdown", - "id": "38862aa9", + "id": "3b7102b6", "metadata": {}, "source": [ "### Indexing and Assignment to Structured arrays\n", @@ -4867,7 +4867,7 @@ { "cell_type": "code", "execution_count": 169, - "id": "3bf8d38a", + "id": "488c3d8e", "metadata": {}, "outputs": [ { @@ -4890,7 +4890,7 @@ }, { "cell_type": "markdown", - "id": "992b7664", + "id": "990b5fc3", "metadata": {}, "source": [ "##### Assignment from Scalars\n", @@ -4901,7 +4901,7 @@ { "cell_type": "code", "execution_count": 170, - "id": "de15da18", + "id": "457cb5e9", "metadata": {}, "outputs": [], "source": [ @@ -4911,7 +4911,7 @@ { "cell_type": "code", "execution_count": 171, - "id": "27427fff", + "id": "38777084", "metadata": {}, "outputs": [ { @@ -4934,7 +4934,7 @@ { "cell_type": "code", "execution_count": 172, - "id": "3a81a352", + "id": "3645344d", "metadata": {}, "outputs": [ { @@ -4956,7 +4956,7 @@ }, { "cell_type": "markdown", - "id": "0e6f1ac4", + "id": "870c9700", "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": "070ff231", + "id": "e8fd808b", "metadata": {}, "outputs": [], "source": [ @@ -4976,7 +4976,7 @@ { "cell_type": "code", "execution_count": 174, - "id": "b4bc2558", + "id": "f3aa49f7", "metadata": {}, "outputs": [], "source": [ @@ -4985,7 +4985,7 @@ }, { "cell_type": "markdown", - "id": "26e6623a", + "id": "fbd36a21", "metadata": {}, "source": [ "```py\n", @@ -5006,7 +5006,7 @@ { "cell_type": "code", "execution_count": 175, - "id": "71d03f9c", + "id": "464b5cd2", "metadata": {}, "outputs": [], "source": [ @@ -5017,7 +5017,7 @@ { "cell_type": "code", "execution_count": 176, - "id": "c4ec0941", + "id": "4c8991c5", "metadata": {}, "outputs": [ { @@ -5039,7 +5039,7 @@ }, { "cell_type": "markdown", - "id": "744b22ab", + "id": "20d76518", "metadata": {}, "source": [ "##### Assignment involving subarrays\n", @@ -5056,7 +5056,7 @@ { "cell_type": "code", "execution_count": 177, - "id": "993e80d4", + "id": "293d1c84", "metadata": {}, "outputs": [ { @@ -5078,7 +5078,7 @@ { "cell_type": "code", "execution_count": 178, - "id": "94068fd8", + "id": "0f9075ff", "metadata": {}, "outputs": [ { @@ -5099,7 +5099,7 @@ }, { "cell_type": "markdown", - "id": "aa3ae581", + "id": "f8222793", "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": "5aa890ea", + "id": "762e56c2", "metadata": {}, "outputs": [ { @@ -5130,7 +5130,7 @@ }, { "cell_type": "markdown", - "id": "2b608a9a", + "id": "4b0cce7a", "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": "eedbd95b", + "id": "fe523c89", "metadata": {}, "outputs": [ { @@ -5159,7 +5159,7 @@ }, { "cell_type": "markdown", - "id": "13b741c2", + "id": "e7d670ae", "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": "16456122", + "id": "40115972", "metadata": {}, "outputs": [], "source": [ @@ -5178,7 +5178,7 @@ { "cell_type": "code", "execution_count": 182, - "id": "2f1109a9", + "id": "e4fbbee4", "metadata": {}, "outputs": [ { @@ -5199,7 +5199,7 @@ { "cell_type": "code", "execution_count": 183, - "id": "21fc9653", + "id": "a15d31c8", "metadata": {}, "outputs": [ { @@ -5219,7 +5219,7 @@ }, { "cell_type": "markdown", - "id": "7a8e2427", + "id": "f5507428", "metadata": {}, "source": [ "##### Accessing multiple fields\n", @@ -5232,7 +5232,7 @@ { "cell_type": "code", "execution_count": 184, - "id": "b2170bad", + "id": "6340151a", "metadata": {}, "outputs": [ { @@ -5254,7 +5254,7 @@ }, { "cell_type": "markdown", - "id": "71392fca", + "id": "8eb64ebd", "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": "a1f70879", + "id": "7bd74c59", "metadata": {}, "outputs": [ { @@ -5287,7 +5287,7 @@ }, { "cell_type": "markdown", - "id": "ccd52130", + "id": "1e0172f9", "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": "e751d459", + "id": "24658ca7", "metadata": {}, "outputs": [], "source": [ @@ -5305,7 +5305,7 @@ }, { "cell_type": "markdown", - "id": "17c3ffee", + "id": "66413646", "metadata": {}, "source": [ "##### Indexing with an integer to get a structured scalar\n", @@ -5316,7 +5316,7 @@ { "cell_type": "code", "execution_count": 187, - "id": "c963d6bf", + "id": "89f9b716", "metadata": {}, "outputs": [ { @@ -5339,7 +5339,7 @@ { "cell_type": "code", "execution_count": 188, - "id": "b5d0b33e", + "id": "dfad9190", "metadata": {}, "outputs": [ { @@ -5359,7 +5359,7 @@ }, { "cell_type": "markdown", - "id": "b2af34f4", + "id": "5d8e449b", "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": "cde65ded", + "id": "db2091a6", "metadata": {}, "outputs": [ { @@ -5391,7 +5391,7 @@ }, { "cell_type": "markdown", - "id": "3843ee4d", + "id": "daa8a5ae", "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": "84a3e293", + "id": "c4018f8b", "metadata": {}, "outputs": [ { @@ -5422,7 +5422,7 @@ { "cell_type": "code", "execution_count": 191, - "id": "0d6c362f", + "id": "56a9900c", "metadata": {}, "outputs": [], "source": [ @@ -5431,7 +5431,7 @@ }, { "cell_type": "markdown", - "id": "6b9bbf7e", + "id": "932d495c", "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": "dba6398a", + "id": "ffe419aa", "metadata": {}, "outputs": [ { @@ -5460,7 +5460,7 @@ }, { "cell_type": "markdown", - "id": "76dfd92d", + "id": "f76611c4", "metadata": {}, "source": [ "#### Viewing structured arrays containing objects\n", @@ -5475,7 +5475,7 @@ { "cell_type": "code", "execution_count": 193, - "id": "9a9c272d", + "id": "068e27af", "metadata": {}, "outputs": [ { @@ -5497,7 +5497,7 @@ }, { "cell_type": "markdown", - "id": "c2166d63", + "id": "6438b816", "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": "80b0d583", + "id": "c10491ed", "metadata": {}, "outputs": [ { @@ -5527,7 +5527,7 @@ }, { "cell_type": "markdown", - "id": "e1bfa6c9", + "id": "30637e7a", "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": "47f739b4", + "id": "3e5d3762", "metadata": {}, "outputs": [ { @@ -5557,7 +5557,7 @@ { "cell_type": "code", "execution_count": 196, - "id": "aaa0d1ad", + "id": "a288e991", "metadata": {}, "outputs": [ { @@ -5577,7 +5577,7 @@ }, { "cell_type": "markdown", - "id": "2b318e45", + "id": "a3daee88", "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": "a3eb03a4", + "id": "c5393a85", "metadata": {}, "outputs": [ { @@ -5608,7 +5608,7 @@ { "cell_type": "code", "execution_count": 198, - "id": "e37a6f82", + "id": "48d95957", "metadata": {}, "outputs": [ { @@ -5628,7 +5628,7 @@ }, { "cell_type": "markdown", - "id": "b3bbca7f", + "id": "8e07d98c", "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": "78dddbf6", + "id": "aa88b878", "metadata": {}, "outputs": [ { @@ -5659,7 +5659,7 @@ { "cell_type": "code", "execution_count": 200, - "id": "3980a4ee", + "id": "a1550171", "metadata": {}, "outputs": [ { @@ -5680,7 +5680,7 @@ { "cell_type": "code", "execution_count": 201, - "id": "5c7c5cfa", + "id": "1088d24b", "metadata": {}, "outputs": [ { @@ -5700,7 +5700,7 @@ }, { "cell_type": "markdown", - "id": "24091496", + "id": "a5c9cefb", "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": "94299542", + "id": "dff0781b", "metadata": {}, "outputs": [ { @@ -5729,7 +5729,7 @@ }, { "cell_type": "markdown", - "id": "aee3bb68", + "id": "835ed799", "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/_sources/data-science/working-with-data/relational-databases.ipynb b/_sources/data-science/working-with-data/relational-databases.ipynb index d2671509bb..dba7d572e7 100644 --- a/_sources/data-science/working-with-data/relational-databases.ipynb +++ b/_sources/data-science/working-with-data/relational-databases.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e8806420", + "id": "d9d8a901", "metadata": {}, "source": [ "# Relational databases\n", diff --git a/_sources/data-science/working-with-data/working-with-data.ipynb b/_sources/data-science/working-with-data/working-with-data.ipynb index 92873ebf74..c522b95844 100644 --- a/_sources/data-science/working-with-data/working-with-data.ipynb +++ b/_sources/data-science/working-with-data/working-with-data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5f5bdd07", + "id": "21f15df1", "metadata": {}, "source": [ "# Working with data\n", diff --git a/_sources/deep-learning/autoencoder.ipynb b/_sources/deep-learning/autoencoder.ipynb index af722d2e48..fd172fcd2c 100644 --- a/_sources/deep-learning/autoencoder.ipynb +++ b/_sources/deep-learning/autoencoder.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9c2c6c42", + "id": "3d68d374", "metadata": {}, "source": [ "# Autoencoder\n", @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53b47613", + "id": "42a70157", "metadata": {}, "outputs": [], "source": [ @@ -118,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "574ad4dc", + "id": "509693a3", "metadata": {}, "source": [ "MNIST Dataset parameters." @@ -127,7 +127,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aad3812f", + "id": "ba32acb9", "metadata": {}, "outputs": [], "source": [ @@ -136,7 +136,7 @@ }, { "cell_type": "markdown", - "id": "9a2e7f92", + "id": "5c2f05c1", "metadata": {}, "source": [ "Training parameters." @@ -145,7 +145,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0246af93", + "id": "bdc6ccf0", "metadata": {}, "outputs": [], "source": [ @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "9c1327af", + "id": "004d47ee", "metadata": {}, "source": [ "Network Parameters" @@ -166,7 +166,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1b7fa0cb", + "id": "79621bf9", "metadata": {}, "outputs": [], "source": [ @@ -176,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "4ac530c4", + "id": "4e891ddd", "metadata": {}, "source": [ "Prepare MNIST data." @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c8c88364", + "id": "c72aadc6", "metadata": {}, "outputs": [], "source": [ @@ -201,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "491c2c40", + "id": "3655bb36", "metadata": {}, "source": [ "Store layers weight & bias.\n", @@ -211,7 +211,7 @@ { "cell_type": "code", "execution_count": null, - "id": "552986d1", + "id": "c33def10", "metadata": {}, "outputs": [], "source": [ @@ -233,7 +233,7 @@ }, { "cell_type": "markdown", - "id": "19fa6d39", + "id": "ea3d4511", "metadata": {}, "source": [ "Building the encoder." @@ -242,7 +242,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dbc1f00b", + "id": "e7138a79", "metadata": {}, "outputs": [], "source": [ @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "b6342243", + "id": "c779bfe4", "metadata": {}, "source": [ "Building the decoder." @@ -267,7 +267,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a46a4512", + "id": "a57b7f3e", "metadata": {}, "outputs": [], "source": [ @@ -283,7 +283,7 @@ }, { "cell_type": "markdown", - "id": "d115d00b", + "id": "6433beb1", "metadata": {}, "source": [ "Mean square loss between original images and reconstructed ones." @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "66a57b39", + "id": "131dd073", "metadata": {}, "outputs": [], "source": [ @@ -302,7 +302,7 @@ }, { "cell_type": "markdown", - "id": "3af38928", + "id": "80004b77", "metadata": {}, "source": [ "Adam optimizer." @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b736b0e9", + "id": "4135df4e", "metadata": {}, "outputs": [], "source": [ @@ -320,7 +320,7 @@ }, { "cell_type": "markdown", - "id": "9e9b302e", + "id": "0dd6cc0c", "metadata": {}, "source": [ "Optimization process." @@ -329,7 +329,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1081a567", + "id": "c1d73d6d", "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ }, { "cell_type": "markdown", - "id": "6e27923c", + "id": "99721c7c", "metadata": {}, "source": [ "Run training for the given number of steps." @@ -362,7 +362,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2b35dbec", + "id": "1febe611", "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "1af3920e", + "id": "0ab91e62", "metadata": {}, "source": [ "Testing and Visualization." @@ -386,7 +386,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bc5edc94", + "id": "b44b5e09", "metadata": {}, "outputs": [], "source": [ @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "42d3bde7", + "id": "16992b29", "metadata": {}, "source": [ "Encode and decode images from test set and visualize their reconstruction." @@ -404,7 +404,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29d08251", + "id": "f5fde75a", "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "da4c2331", + "id": "2325f546", "metadata": {}, "source": [ "

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  • - Ocademy Open Machine Learning Book
  • - Ocademy Open Machine Learning Book class="headerbtn" data-toggle="tooltip" data-placement="left" -title="Download notebook file" -> - - - - - -.ipynb - - -
    • - -
  • - @@ -1495,7 +1478,7 @@

    Ocademy Open Machine Learning Book

    -.md +.ipynb
    • @@ -1832,16 +1815,27 @@

    Contents

    -
    +
    +
    +
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython
+
    +
    +
    +
    +

    27. Deep Q-learning#

    27.1. Overview#

    Usually, we regard Deep Q Network as DQN, and it can also be called Reinforcement Learning (RL) whose aim is to achieve the desired behavior of an agent that learns from its mistakes and improves its performance.

    RL is a type of machine learning that allows us to create AI agents that learn from the environment by interacting with it to maximize its cumulative reward. Here is an image showing the basic RL operation principle:

    -
    -../_images/02_pipline.png +
    +https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png
    -

    Fig. 27.1 Pipline of DQN#

    +

    Fig. 27.1 Pipline of DQN#

    From this image, we can see that at each step \(k\), the agent picks an action \(u_k\), receives an observation \(y_k\) and receives a reward \(r_k\), and the environment receives an action \(u_k\), emits an observation \(y_{k+1}\) and emits a reward \(r_{k+1}\). Later, the time increments k ← k + 1. A one step time delay is assumed between executing the action and receiving the observation as well as reward. We assume that the resulting time interval \(∆t = t_k − t_{k+1}\) is constant.

    @@ -1961,10 +1955,10 @@

    27.2.5.1. Example

    Reference value is part of the environment state: \(x_k =[y_k y^∗_k]^T\).

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