#计算损失 defcomputeCost(X,y,theta):m=y.shape[0]#J=(np.sum((X.dot(theta)-y)**2))/(2*m)C=X.dot(theta)-yJ2=(C.T.dot(C))/(2*m)returnJ2#梯度下降 defgradientDescent(X,y,theta,alpha,num_iters):m=y.shape[0]#print(m)# 存储历史误差 J_history=np.zeros((num_iters,1))f...
m=y.shape[0] # J = (np.sum((X.dot(theta) - y)**2)) / (2*m) C=X.dot(theta)-y J2=(C.T.dot(C))/(2*m) returnJ2 #梯度下降 defgradientDescent(X, y, theta, alpha, num_iters): m=y.shape[0] #print(m) # 存储历史误差 J_history=np.zeros((num_iters,1)) foriterin...
m=y.shape[0] # J = (np.sum((X.dot(theta) - y)**2)) / (2*m) C=X.dot(theta)-y J2=(C.T.dot(C))/(2*m) returnJ2 #梯度下降 defgradientDescent(X, y, theta, alpha, num_iters): m=y.shape[0] #print(m) # 存储历史误差 J_history=np.zeros((num_iters,1)) foriterin...
(1) 多维特征 & 多变量梯度下降 (2)多维梯度下降的例子:特征缩放(Feature Scaling) (3)学习率(Learning rate) (4)正规方程(Normal equation) (5)python代码实现 学习完了机器学习的多变量线性回归课程,现在来将所学记录下来。 多变量线性回归(Linear Regression with multiple variables) (1) 多维特征 & 多变...
max_num = max_num @timer def waste_time(self, num_times): for _ in range(num_times): sum([number**2 for number in range(self.max_num)]) Using this class, you can see the effect of the decorators:Python >>> from class_decorators import TimeWaster >>> tw = TimeWaster(1000) ...
[coord_row-window_ext:coord_row+window_ext+1, coord_col-window_ext:coord_col+window_ext+1, :] if window_original.shape == window_warped.shape: SSD = np.sum(weights * (window_original - window_warped)**2) SSDs.append(SSD) min_idx = np.argmin(SSDs) if len(SSDs) > 0 else ...
Now let see how we can use multiple variables for different values. So from above we know that x which represents value 20 and now we will take another variable called ‘y’ where we will assign value 10 to it and do a sum for x+y then the result will be 30 i.e. 10+20= 30....
n - 1. This is useful if you areconcatenating objects where the concatenation axis does not havemeaningful indexing information. Note the index values on the otheraxes are still respected in the join.keys : sequence, default NoneIf multiple levels passed, should contain tuples. Constructhierarchic...
数学定义: \frac{\sum_{i=0}^{n}(x_i-\bar{x})}{n}, \bar{x} 是平均值。x_i:i=1,2,...,n 是n 个数。 python代码:用np.var()函数来实现。 笔算结果: [1,3] 的方差为 \frac{(1-2)^2+(3-2)^2}{2}=1 [2,4] 的方差为 \frac{(2-3)^2+(4-3)^2}{2}=1 python代...
\begin{aligned} \sum_{i=1}^{5}{c_{i}x_{i}} \leqslant 10 \end{aligned} 则代码如下 model.addCons(quicksum(x[i] for i in range(len(c))) <= 10, "Width") 更为复杂的情况,可以见下面的例子 \begin{aligned} \min\text{ }\sum_i{\sum_j{c_{ij}x_{ij}}} \\ \sum_{i\in...