def predict(self, X):
X = np.asanyarray(X)[:, :self.num_steps]
X = np.asanyarray(X, dtype=np.float64, order='C')
P = _trend.predict(X, self.R, self.labels, self.num_labels, self.gamma)
return P.argmax(axis=1)
def predict(self, X):
X = np.asanyarray(X)[:, :self.num_steps]
X = np.asanyarray(X, dtype=np.float64, order='C')
P = _trend.predict(X, self.R, self.labels, self.num_labels, self.gamma)
return P.argmax(axis=1)
def predict_proba(self, X):
X = np.asanyarray(X)[:, :self.num_steps]
X = np.asanyarray(X, dtype=np.float64, order='C')
P = _trend.predict(X, self.R, self.labels, self.num_labels, self.gamma)
P = ((P.T / P.sum(axis=1)).T)
return P
def predict_proba(self, X):
X = np.asanyarray(X)[:, :self.num_steps]
X = np.asanyarray(X, dtype=np.float64, order='C')
P = _trend.predict(X, self.R, self.labels, self.num_labels, self.gamma)
P = ((P.T / P.sum(axis=1)).T)
return P