J_history=np.zeros((num_iters,1))foriterinrange(num_iters):# 对J求导,得到 alpha/m*(WX-Y)*x(i),(3,m)*(m,1)X(m,3)*(3,1)=(m,1)theta=theta-(alpha/m)*(X.T.dot(X.dot(theta)-y))J_history[iter]=computeCost(X,y,theta)returnJ_history,theta iterations=10000#迭代次数 alph...
在练习中,一个称为“fminunc”的Octave函数是用来优化函数来计算成本和梯度参数。由于我们使用Python,我们可以用SciPy的“optimize”命名空间来做同样的事情。scipy中的optimize子包中提供了常用的最优化算法函数实现,我们可以直接调用这些函数完成我们的优化问题。在用python实现逻辑回归和线性回归时,使用梯度下降法最小化c...
*x' * (g(z)-y); theta=theta-H\delta %theta=theta-inv(H)*delta end % Plot Newton's method result % Only need 2 points to define a line, so choose two endpoints plot_x = [min(x(:,2))-2, max(x(:,2))+2]; % Calculate the decision boundary line plot_y = (-1./theta(...
plt.plot(myline, mymodel(myline)) plt.show() Result: Run example » Example Explained Import the modules you need. You can learn about the NumPy module in ourNumPy Tutorial. You can learn about the SciPy module in ourSciPy Tutorial. ...
Recall: If there are patients who have diabetes in the test set and your logistic regression model can identify it 57% of the time. ROC curve Receiver Operating Characteristic (ROC) curve is a plot of the true positive rate against the false positive rate. It shows the tradeoff between sens...
In Python, a probability plot can be generated with the command stats.probplot(data, plot=plt) https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.probplot.html a) Probability-Plots 用于可视化评估分布,绘制分位点来比较概率分布 ...
python程序import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D M = 3 #属性个数+1 属性加 偏移项b, 一个3个参数 N = 50#二分类。每类样本N个 #随机生成两个属性的N个第一类样本 feature11 = np.random.randint(0, 10, size = N)...
ax.plot(line_x,line_y)defpredict(self,x): x=np.asarray(x, np.float32) x=x.reshape(x.shape[0],1) x=np.hstack((x,np.ones((x.shape[0],1)))returnnp.dot(x,self._w)if__name__=="__main__": Regression=Linear_Regression() Regression...
plt.ylabel("y_predict_in_test") plt.plot([-10,60],[-10,60],'k--') plt.show() 输出值: C:\Users\asus\AppData\Local\Programs\Python\Python35-32\python.exe "D:/BaiduYunDownload/python_exe/daily exercise/OpenCV and MachineLearning/Linear_regression.py" ...
(lSet, leafType, errType, ops) retTree['right'] = createTree(rSet, leafType, errType, ops) return retTree if __name__ == "__main__": myDat = mat(loadDataSet('5.2test.txt')) print(createTree(myDat)) # 绘制数据点图 import matplotlib.pyplot as plt plt.plot(myDat[:, 0], ...