def fit(self, x, y):
x1 = Accumulation.agom(y, None, True)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
x1_0 = x1[0:-1, 0]
x1_0 = x1_0.reshape([-1, 1])
x1_n = x1[1:, 1:]
B_x = ModelMethod.construct_matrix(-x1_0, x1_n)
B = ModelMethod.construct_matrix(-B_x, ones_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, x, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethod.based(x1)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
range_array = np.arange(len(x) - 1)
range_array = range_array.reshape([-1, 1])
B1 = ModelMethod.construct_matrix(-z1, range_array)
B = ModelMethod.construct_matrix(-B1, ones_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, x1)
self.x1 = x1
return self
def fit(self, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethod.get_backvalue(x1)
z1_square = np.power(z1, self.n)
B = ModelMethod.construct_matrix(z1, z1_square)
self.x_orig = y
self.params = ModelMethod.get_params(B, y)
return self
def fit(self, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethod.get_backvalue(x1)
z1_square = np.power(z1, self.n)
B = ModelMethod.construct_matrix(z1, z1_square)
self.x_orig = y
self.params = ModelMethod.get_params(B, y)
return self
def fit(self, x, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethod.get_backvalue(x1)
arange_array = x[1:]
arange_array = arange_array.reshape([-1, 1])
B = ModelMethod.construct_matrix(z1, arange_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, y)
return self
def fit(self, x, y):
x1 = Accumulation.ago(y, None, True)
z1 = ModelMethod.get_backvalue(x1)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
B = ModelMethod.construct_matrix(z1, ones_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, y)
return self
def fit(self, x, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethod.based(x1)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
B = ModelMethod.construct_matrix(-z1, ones_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, x1)
return self
def fit(self, x, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethod.get_backvalue(x1)
arange_array = x[1:]
arange_array = arange_array.reshape([-1, 1])
B = ModelMethod.construct_matrix(z1, arange_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, y)
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
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
x1_0 = x1[0:-1, 0]
x1_0 = x1_0.reshape([-1, 1])
x1_n = x1[1:, 1:]
B_x = ModelMethod.construct_matrix(-x1_0, x1_n)
B = ModelMethod.construct_matrix(-B_x, ones_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, x, y):
x1 = Accumulation.agom(y, None, True)
x1_0 = x1[0:, 0]
z1 = ModelMethod.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethod.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
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 = ModelMethod.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethod.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethod.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1