def _predict(self, X, theta):
w = theta[:-1]
b = theta[-1]
logit = np.dot(X, w) + b
return sigmoid(logit)
def predict(self, X):
w = self.theta[:-1]
b = self.theta[-1]
# return (np.random.default_rng().uniform(size=X.shape[0]) < sigmoid(
# np.dot(X, w) + b)).astype(np.int)
return (sigmoid(np.dot(X, w) + b) > 0.5).astype(np.int)
def predict(self, X):
w = self.theta[:-1]
b = self.theta[-1]
return (sigmoid(np.dot(X, w) + b) > 0.5).astype(np.int)