def predict(self, Xtest): probs = np.random.rand(Xtest.shape[0]) ytest = utils.threshold_probs(probs) return ytest
def predict(self, Xtest): probs = utils.sigmoid(Xtest.dot(self.weights)) ytest = utils.threshold_probs(probs) return ytest
def predict(self, Xtest): ytest = utils.sigmoid(np.dot(Xtest, self.weights)) ytest = utils.threshold_probs(ytest) return ytest
def predict(self, Xtest): Rbf = RBF() Xtest = Rbf.transform(Xtest) ytest = utils.sigmoid(np.dot(Xtest, self.weights)) ytest = utils.threshold_probs(ytest) return ytest
def predict(self, Xtest): xw = np.dot(Xtest, self.weights) sqrt_one_plus_xwSquare = utils.sqrt_one_plus_xwSquare p = 0.5 * (1 + xw/sqrt_one_plus_xwSquare(xw)) p = utils.threshold_probs(p) return p
def predict(self, Xtest): ytest = 0.5 * ( 1 + np.divide(np.dot(Xtest, self.weights), np.sqrt(1 + np.square(np.dot(Xtest, self.weights))))) ytest = utils.threshold_probs(ytest) return ytest
def predict(self, Xtest, weights=None): if weights is None: weights = self.weights p = utils.sigmoid(np.dot(Xtest, weights)) p = utils.threshold_probs(p) return p
def predict(self, Xtest): probabilities = utils.custom_probs(np.dot(self.weights, Xtest.T)) return utils.threshold_probs(probabilities)
def predict(self, Xtest): ytest = [] for i in range(0, len(Xtest)): ytest.append(self.evaluate(Xtest[i])) ytest = np.array(ytest) return utils.threshold_probs(ytest)
def predict(self, Xtest): xw = np.dot(Xtest, self.weights) sqrt_one_plus_xwSquare = utils.sqrt_one_plus_xwSquare p = 0.5 * (1 + xw / sqrt_one_plus_xwSquare(xw)) p = utils.threshold_probs(p) return p
def predict(self, Xtest): p = utils.sigmoid(np.dot(Xtest, self.weights)) p = utils.threshold_probs(p) return p