def fit(self, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethods.get_backvalue(x1)
z1_square = np.power(z1, self.n)
B = ModelMethods.construct_matrix(z1, z1_square)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethods.get_backvalue(x1)
z1_square = np.power(z1, self.n)
B = ModelMethods.construct_matrix(z1, z1_square)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethods.get_backvalue(x1)
arange_array = x[1:]
arange_array = arange_array.reshape([-1, 1])
B = ModelMethods.construct_matrix(z1, arange_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethods.get_backvalue(x1)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
B = ModelMethods.construct_matrix(z1, ones_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethods.get_backvalue(x1)
arange_array = x[1:]
arange_array = arange_array.reshape([-1, 1])
B = ModelMethods.construct_matrix(z1, arange_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
x1 = Accumulation.agom(y, None, True)
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, y, x):
x_train = x[0:, 1:]
y_conect = np.concatenate((y, x_train), axis=1)
x1 = Accumulation.agom(y_conect, None, True)
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
self.B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(self.B,
np.array(y_conect)[0:, 0])
self.x1 = x1
self.is_fitted = True
return self
def fit(self, x, y):
lens_1 = len(y[0, 0:])
y1 = y.T
x1 = np.zeros(y1.shape)
for i in range(0, lens_1):
x1[i, 0:] = FractionalAccumulation.fago(y1[i, 0:], self.r)
x1 = x1.T
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, y, x):
x_train = x[0:, 1:]
y_conect = np.concatenate((y, x_train), axis=1)
lens_1 = len(y_conect[0, 0:])
y1 = y_conect.T
x1 = np.zeros(y1.shape)
for i in range(0, lens_1):
x1[i, 0:] = FractionalAccumulation.fago(y1[i, 0:], self.r)
x1 = x1.T
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1