In [4]: svc = SVC()
In [5]: import numpy as np
...: X = np.array([[-1, -1, 2], [-2, -1, 4], [1, 1, 5], [2, 1, 2]]) #Тут массив из 3
...: y = np.array([2, 1, 1, 1])
In [6]: svc.fit(X, y)
Out[6]:
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
In [7]: svc.classes_
Out[7]: array([1, 2])result = []
for i in list2:
tmp = [i] * len(list1)
result += list(zip(list1, tmp))class Dense(Layer):
def __init__(self, units):
super(Dense, self).__init__(units)
self.W = None
self.b = None
def fprop(self, inputs, pass_type='train'):
self.inputs = inputs
if self.W is None:
self.W = np.random.uniform(low=-0.01, high=0.01, size=(self.units, inputs.shape[1]))
self.b = np.random.uniform(low=-0.01, high=0.01, size=self.units)
return np.dot(inputs, self.W.T) + self.b
def bprop(self, outputs_deriv):
return np.dot(outputs_deriv, self.W)
def update_weights(self, outputs_deriv, learning_rate):
self.W -= learning_rate * np.dot(outputs_deriv.T, self.inputs)
self.b -= learning_rate * outputs_deriv.sum(axis=0)# forward-propagation step
output = x_batch
for layer in self.layers:
output = layer.fprop(output, pass_type='train')
# backward-propagation step
outputs_deriv = self.loss.grad(y_batch, output)
for layer in self.layers[::-1]:
layer.update_weights(outputs_deriv, learning_rate)
outputs_deriv = layer.bprop(outputs_deriv)