使用动画将每一步的路径展示出来,我们使用animation.FuncAnimation类来完成动画模拟,然后使用.to_jshtml方法将动画显示出来。 path = path_list_gd #梯度下降法的优化路径 fig, ax = plt.subplots(figsize=(6, 6)) line, = ax.plot([], [], 'b', label='Gradient Descent', lw=2) #保存路径 point, ...
这部分内部参考了机器学习中的数学(1)-回归(regression)、梯度下降(gradient descent)。假设有x1x1,x2x2,..., xnxn共nn个feature,θθ为xx的系数,则 拟合函数 hθ(x)=θ0+θ1x1+...+θnxn=θTx,其中x0=1拟合函数 hθ(x)=θ0+θ1x1+...+θnxn=θTx,其中x0=1 误差函数 J(θ)=12∑i=1m(...
new_m = m_current -(learningRate * m_gradient)# 沿梯度负方向 return[new_b, new_m] 其中learningRate是学习速率,它决定了逼近最低点的速率。可以想到的是,如果learningRate太大,则可能导致我们不断地最低点附近来回震荡;而learningRate太小,则会导致逼近的速度太慢。An Introduction to Gradient Descent a...
# Stochastic gradient descent to get optimized P and Q matrix def sgd(self): for i, j, r in self.samples: prediction = self.get_rating(i, j) e = (r - prediction) self.b_u[i] += self.alpha * (e - self.beta * self.b_u[i]) self.b_i[j] += self.alpha * (e - self...
从机器学习的角度来说,以上的数据只有一个feature,所以用一元线性回归模型即可。这里我们将一元线性模型的结论一般化,即推广到多元线性回归模型。这部分内部参考了机器学习中的数学(1)-回归(regression)、梯度下降(gradient descent)。假设有x1,x2,…,xn共n...
The Rajeswaran paper algorithm does gradient ascent instead of descent, which is why the signs are how they are. Tagged cartpole, natural policy gradient, programming, tensorflow Jan 19 2018 8 Comments Learning, motor control, Nengo, neuroscience, Operational space control, Python Building a spi...
show() optimal = gradient_descent(X, y, alpha) print('optimal:', optimal) print('error function:', error_function(optimal, X, y)[0, 0]) 代码就不过多解释了,基本都是简书上面拷贝过来的,简书上面解释已经足够详细了。对新手比较陌生的,是矩阵与坐标的表示形式,比如a和b两个N*1维矩阵,转成...
ax.set_zlabel('Z')# Creating the Animation objectline_ani = animation.FuncAnimation(fig, update_line, nb_steps+1, fargs=(data, line), \ interval=200, blit=False)# line_ani.save('gradient_descent.gif', dpi=80, writer='imagemagick')plt.show() ...
Due to the gradient descent methods, we can affirm that the optimal solution is a local minimum. However, it is impossible to know if a global minimum was found. For highly non-linear problems, there might exist a wide range of local optima. Solving the same problem with different initial...
(default: {None}) """ assert method in ("batch", "stochastic"), str(method) # Batch gradient descent. if method == "batch": self._batch_gradient_descent(data, label, learning_rate, epochs) # Stochastic gradient descent. if method == "stochastic": self._stochastic_gradient_descent( ...