code:perceptron
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code:perceptron [2016/08/09 10:25] 127.0.0.1 external edit |
code:perceptron [2016/09/08 11:04] (current) asa |
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| Line 47: | Line 47: | ||
| self.converged = converged | self.converged = converged | ||
| if converged : | if converged : | ||
| - | print 'converged in %d iterations ' % iterations | + | print ('converged in %d iterations ' % iterations) |
| def discriminant(self, x) : | def discriminant(self, x) : | ||
| - | return np.dot(self.w, x) | + | return np.inner(self.w, x) |
| | | ||
| def predict(self, X) : | def predict(self, X) : | ||
| Line 63: | Line 63: | ||
| """ | """ | ||
| | | ||
| - | scores = np.dot(self.w, X) | + | scores = np.inner(self.w, X) |
| return np.sign(scores) | return np.sign(scores) | ||
| def generate_separable_data(N) : | def generate_separable_data(N) : | ||
| - | xA,yA,xB,yB = [np.random.uniform(-1, 1) for i in range(4)] | ||
| w = np.random.uniform(-1, 1, 2) | w = np.random.uniform(-1, 1, 2) | ||
| - | print w,w.shape | + | print (w,w.shape) |
| X = np.random.uniform(-1, 1, [N, 2]) | X = np.random.uniform(-1, 1, [N, 2]) | ||
| - | print X,X.shape | + | print (X,X.shape) |
| y = np.sign(np.dot(X, w)) | y = np.sign(np.dot(X, w)) | ||
| return X,y,w | return X,y,w | ||
| Line 91: | Line 90: | ||
| 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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