fix includes

MANIFEST

@@ -4,3 +4,13 @@ setup.py
 micromlgen/__init__.py
 micromlgen/micromlgen.py
 micromlgen/micromlgen_test.py
+micromlgen/templates/binary_classification.jinja
+micromlgen/templates/classmap.jinja
+micromlgen/templates/compute_class.jinja
+micromlgen/templates/compute_decisions.jinja
+micromlgen/templates/compute_kernels.bck.jinja
+micromlgen/templates/compute_kernels.jinja
+micromlgen/templates/compute_votes.jinja
+micromlgen/templates/kernel_function.jinja
+micromlgen/templates/self_test.jinja
+micromlgen/templates/svm.jinja

MANIFEST.in

@@ -0,0 +1 @@
+include micromlgen/templates/*

README.md

@@ -27,4 +27,60 @@ fill that much space.
 After a whole 20 minutes of thinking, the result is a Python library that takes 
 a SVM classifier and produces C code you can copy-paste in your project to run ML 
 on microcontrollers. The C code is clearly readable, so you can even see what's going on:
-no black magic.
+no black magic.
+
+## Install
+
+`pip install micromlgen`
+
+## Use
+
+```python
+from micromlgen import port
+from sklearn.svm import SVC
+from sklearn.datasets import load_iris
+
+
+if __name__ == '__main__':
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    clf = SVC(kernel='linear').fit(X, y)
+    print(port(clf))
+```
+
+You may pass a classmap to get readable class names in the ported code
+
+```python
+from micromlgen import port
+from sklearn.svm import SVC
+from sklearn.datasets import load_iris
+
+
+if __name__ == '__main__':
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    clf = SVC(kernel='linear').fit(X, y)
+    print(port(clf, classmap={
+        0: 'setosa',
+        1: 'virginica',
+        2: 'versicolor'
+    }))
+```
+
+You can pass a test set to generate self test code
+
+```python
+from micromlgen import port
+from sklearn.svm import SVC
+from sklearn.datasets import load_iris
+
+
+if __name__ == '__main__':
+    iris = load_iris()
+    X_train, X_test = iris.data[:-10, :], iris.data[-10:, :]
+    y_train, y_test = iris.target[:-10], iris.target[-10:]
+    clf = SVC(kernel='linear').fit(X_train, y_train)
+    print(port(clf, test_set=(X_test, y_test)))
+```

micromlgen/micromlgen.py

@@ -27,7 +27,7 @@ def port(clf, test_set=None, classmap=None, **kwargs):
         }
     }
     loader = FileSystemLoader('.')
-    template = Environment(loader=loader).get_template('templates/svm.jinja')
+    template = Environment(loader=loader).get_template('./templates/svm.jinja')
     code = template.render(template_data)
     code = re.sub(r'\n\s*\n', '\n', code)
 

setup.py

@@ -2,13 +2,13 @@
 setup(
   name = 'micromlgen',
   packages = ['micromlgen'],
-  version = '0.3',
+  version = '0.4',
   license='MIT',
   description = 'Generate C code for microcontrollers from Python\'s sklearn classifiers',
   author = 'Simone Salerno',
   author_email = '[email protected]',
   url = 'https://github.com/agrimagsrl/micromlgen',
-  download_url = 'https://github.com/agrimagsrl/micromlgen/archive/v_03.tar.gz',
+  download_url = 'https://github.com/agrimagsrl/micromlgen/archive/v_04.tar.gz',
   keywords = ['ML', 'microcontrollers', 'sklearn', 'machine learning'],
   install_requires=[
           'jinja2',