def init_params(self, n_features):
 """
 初始化参数
 :return:
 """
 self.w = np.random.random(size=(n_features + 1, 1))
def fit(self, x, y):
	if self.mode=='dual':
		self._dual_fit(x,y,epochs,eta)
	else:
 """
 :param x: ndarray格式数据: m x n
 :param y: ndarray格式数据: m x 1
 :return:
 """
		# 设置学习率
		if self.eta is None:
			self.eta = max(1e-2, 1.0 / np.sqrt(x.shape[0]))
		y = y.reshape(-1, 1)
		y[y == 0] = -1
		# 初始化参数w,b
		n_samples, n_features = x.shape
		self.init_params(n_features)
		x = np.c_[x, np.ones(shape=(n_samples,))]
		x_y = np.c_[x, y]
		for _ in range(self.epochs):
			error_sum = 0
			np.random.shuffle(x_y)
			for index in range(0, n_samples):
				x_i = x_y[index, :-1]
				y_i = x_y[index, -1:]
				# 更新错分点的参数
				if (x_i.dot(self.w) * y_i)[0] < 0:
					dw = (-x_i * y_i).reshape(-1, 1)
					self.w = self.w - self.eta * dw
					error_sum += 1
			if error_sum == 0:
				break
	def _dual_fit(self, x, y):
		"""
		模型训练的对偶形式
		:param x:
		:param y:
		:return:
		"""
		y = y.reshape(-1, 1)
		y[y == 0] = -1
		n_samples, n_features = x.shape
		# 初始化参数
		self.alpha = np.zeros(shape=(n_samples, 1))
		x = np.c_[x, np.ones(shape=(n_samples,))]
		for _ in range(self.epochs):
			error_sum = 0
			indices = list(range(0, n_samples))
			np.random.shuffle(indices)
			for index in indices:
				x_i = x[index, :]
				y_i = y[index]
				# 更新错分点的参数,(注意需要有等号,因为初始化的alpha全为0)
				if (x_i.dot(x.T.dot(self.alpha * y)) * y_i)[0] <= 0:
					self.alpha[index] += self.eta
					error_sum += 1
			if error_sum == 0:
				break
		# 更新回w
		self.w = x.T.dot(alpha * y)
def get_params(self):
 """
 输出原始的系数
 :return: w
 """
 return self.w
def predict(self, x):
 """
 :param x:ndarray格式数据: m x n
 :return: m x 1
 """
 n_samples = x.shape[0]
 x = np.c_[x, np.ones(shape=(n_samples,))]
 return (x.dot(self.w) > 0).astype(int)
def predict_proba(self, x):
 """
 :param x:ndarray格式数据: m x n
 :return: m x 1
 """
 n_samples = x.shape[0]
 x = np.c_[x, np.ones(shape=(n_samples,))]
 return utils.sigmoid(x.dot(self.w))
def plot_decision_boundary(self, x, y):
 """
 绘制前两个维度的决策边界
 :param x:
 :param y:
 :return:
 """
 weights = self.get_params()
 w1 = weights[0][0]
 w2 = weights[1][0]
 bias = weights[-1][0]
 x1 = np.arange(np.min(x), np.max(x), 0.1)
 x2 = -w1 / w2 * x1 - bias / w2
 plt.scatter(x[:, 0], x[:, 1], c=y, s=50)
 plt.plot(x1, x2, 'r')
 plt.show()