code:perceptron
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| Both sides previous revision Previous revision Next revision | Previous revision Last revision Both sides next revision | ||
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code:perceptron [2015/08/28 10:36] asa |
code:perceptron [2016/08/09 10:25] 127.0.0.1 external edit |
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| Line 5: | Line 5: | ||
| <file python perceptron.py> | <file python perceptron.py> | ||
| - | |||
| import numpy as np | import numpy as np | ||
| Line 21: | Line 20: | ||
| def fit(self, X, y) : | def fit(self, X, y) : | ||
| + | """ | ||
| + | Train a classifier using the perceptron training algorithm. | ||
| + | After training the attribute 'w' will contain the perceptron weight vector. | ||
| + | Parameters | ||
| + | ---------- | ||
| + | |||
| + | X : ndarray, shape (num_examples, n_features) | ||
| + | Training data. | ||
| + | |||
| + | y : ndarray, shape (n_examples,) | ||
| + | Array of labels. | ||
| + | | ||
| + | """ | ||
| self.w = np.zeros(len(X[0])) | self.w = np.zeros(len(X[0])) | ||
| converged = False | converged = False | ||
| Line 31: | Line 43: | ||
| self.w = self.w + y[i] * self.learning_rate * X[i] | self.w = self.w + y[i] * self.learning_rate * X[i] | ||
| converged = False | converged = False | ||
| + | plot_data(X, y, self.w) | ||
| iterations += 1 | iterations += 1 | ||
| - | plot_data(X, y, self.w) | ||
| self.converged = converged | self.converged = converged | ||
| if converged : | if converged : | ||
| Line 41: | Line 53: | ||
| | | ||
| def predict(self, X) : | def predict(self, X) : | ||
| + | """ | ||
| + | make predictions using a trained linear classifier | ||
| + | Parameters | ||
| + | ---------- | ||
| + | |||
| + | X : ndarray, shape (num_examples, n_features) | ||
| + | Training data. | ||
| + | """ | ||
| + | | ||
| scores = np.dot(self.w, X) | scores = np.dot(self.w, X) | ||
| return np.sign(scores) | return np.sign(scores) | ||
| - | |||
| def generate_separable_data(N) : | def generate_separable_data(N) : | ||
| Line 68: | Line 88: | ||
| if __name__=='__main__' : | if __name__=='__main__' : | ||
| - | X,y,w = generate_separable_data(20) | + | X,y,w = generate_separable_data(40) |
| p = Perceptron() | p = Perceptron() | ||
| p.fit(X,y) | p.fit(X,y) | ||
| </file> | </file> | ||
code/perceptron.txt ยท Last modified: 2016/09/08 11:04 by asa
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