coeff = np.random.randint(4, size= (n_objects, n_features)) print(X_train0:2) print(coeff) print(np.sum(coeff*X_train, axis=2))

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coeff = np.random.randint(4,size=(4,3)) intercept = np.random.rand(3) print(intercept) print(y_20:5) print(x_20:5) print(np.dot(x_20:5, coeff)+intercept) value = np.array(np.dot(x_20:5, coeff), dtype=theano.config.floatX) m = theano.shared(value, name='m', borrow=True) y = theano.tensor.nnet.softmax(m) print(y.eval())

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