名称错误：未定义名称“test\u soft”。公式在那里，但我不能运行

Question

我有一个错误，说函数没有定义，但是有定义。我不知道为什么会这样。没有空格等。所有这3个def都已经用代码编写，但是python不能运行它们。你知道为什么吗。 请那些不知道如何回答，请不要张贴任何无关的答案，这个问题！谢谢您！你知道吗

import numpy as np
import cvxopt
import cvxopt.solvers
from numpy import linalg

def linear_kernel (x1,x2):
    return np.dot(x1, x2)

def polynominal_kernel(x,y,p=3):
    return(1+np.dot(x,y))**p
def gaussian_kernel(x, y, sigma=5.0):
    return np.exp(-linalg.norm(x-y)**2 / (2*(sigma**2)))

class SVM(object):

    def  __init__(self, kernel=linear_kernel, C=None):
        self.kernel=kernel
        self.C=C
        if self.C is not None: self.C = float(self.C)

    def fit(self, X, y):
        n_samples, n_features= X.shape

        K=np.zeros((n_samples, n_samples))
        for i in range(n_samples):
            for j in range (n_samples):
                K[i,j]=self.kernel(X[i], X[j])

        P=cvxopt.matrix(np.outer(y,y)*K)
        q=cvxopt.matrix(np.ones(n_samples)*-1)
        A=cvxopt.matrix(y, (1,n_samples))
        b=cvxopt.matrix(0.0)

        if self.C is None:
            G=cvxopt.matrix(np.diag(np.ones(n_samples)*-1))
            h=cvxopt.matrix(np.diag(np.zeros(n_samples)))
        else:
            tmp1=   np.diag(np.ones(n_samples)*-1)
            tmp2=np.identity(n_samples)
            G=cvxopt.matrix(np.vstack((tmp1,tmp2)))
        solution=cvxopt.solvers.qp(P,q,G,h,A,b) 
        a=np.ravel(solution['x'])

        sv=a>1e-5
        ind=np.arange(len(a)) [sv]
        self.a =a[sv]
        self.sv=X[sv]
        self.sv_y=y[sv]
        print("%d support vectors out of %d points" %(len(self.a), n_samples))

        self.b=0
        for n in range(len(self.a)):
            self.b +=self.sv_y[n]
            self.b -=np.sum(self.a*self.sv_y*K[ind[n],sv])
        self.b /=len(self.a)

        if self.kernel==linear_kernel:
            self.w=np.zeros(n_features)
            for n in range (len(self.a)):
                self.w +=self.a[n]*self.sv_y[n]*self.sv[n]
            else:
                self.w=None
    def project(self,X):
        if self.w is not None:
            return np.dot(X,self.w)+self.b
        else:
            y_predict=np.zeros(len(X))
            for i in range(len(X)):
                s=0
                for a, sv_y, sv in zip(self.a, self.sv_y, self.sv):
                    s +=a*sv_y*self.kernel(X[i], sv)
                y_predict[i]=s
            return y_predict+self.b
    def predict (self,X):
        return np.sign(self.project(X))



if __name__=="main":
    import pylab as pl 

    def gen_lin_separable_data():
        mean1 =np.array([0,2])
        mean2=np.array([2,0])
        cov=np.array([[0.8, 0.6], [0.6, 0.8]])
        X1=np.random.multivariate_normal(mean1, cov, 100)
        y1=np.ones(len(X1))
        X2=np.random.multivariate_normal(mean2, cov, 100)
        y2=np.ones(len(X2)) * -1
        return X1, y1,X2, y2
    def gen_non_lin_separable_data():
        mean1=[-1,2]
        mean2=[1,-1]
        mean3=[4,-4]
        mean4=[-4,4]
        cov=[[1.0,0.8], [0.8,1.0]]
        X1=np.random.multivariate_normal(mean1, cov, 50)
        X1=np.vstack((X1, np.random.multivariate_normal(mean3, cov, 50)))
        y1=np.ones(len(X1))
        X2=np.random.multivariate_normal(mean2, cov, 50)
        X2.vstack((X2,np.random.multivariate_normal(mean4, cov, 50)))
        y2=np.ones(len(X2))*-1
        return X1, y1, X2, y2

    def gen_lin_separable_overlap_data():
        mean1 = np.array([0,2])
        mean2=np.array([2,0])
        cov=np.array([1.5, 1.0], [1.0, 1.5])
        X1=np.random.multivariate_normal(mean1, cov, 100)
        y1=np.ones(len(X1))
        X2=np.random.multivariate_normal(mean2, cov, 50)
        X2.vstack((X2,np.random.multivariate_normal(mean4, cov, 100)))
        y2=np.ones(len(X2))*-1
        return X1, y1, X2, y2
    def split_train(X1, y1, X2, y2):
        X1_train=X1[90:]
        y1_train=y1[:90]
        X2_train=X2[:90]
        y2_train=y2[:90]
        X_train=np.vstack((X1_train, X2_train))
        y_train=np.hstack((y1_train, y2_train))
        return X_train, y_train
    def split_test(X1, y1, X2, y2):
        X1_test=X1[90:]
        y1_test=y1[90:]
        X2_test=X2[90:]
        y2_test=y2[90:]
        X_test=np.vstack((X1_test, X2_test))
        y_test=np.hstack((y1_test, y2_test))
        return X_test, y_test

    def plot_margin(X1_train, X2_train, clf):
        def f(x, w, b, c=0):
            return (-w[0]*x-b+c)/w[1]

        pl.plot(X1_train[:,0], X1_train[:,1], "ro")
        pl.plot(X2_train[:,0], X2_train[:,1], "bo")
        pl.scatter(clf.sv[:,0], clf.sv[:,1], s=100, c="g")

        a0=-4; a1=f(a0, clf.w, clf.b)
        b0=4;b1=f(bo, clf.w, clf.b)
        pl.plot([a0, b0],[a1, b1], "k")

        a0=-4; a1=f(a0, clf.w, clf.b,1)
        b0=4;b1=f(bo, clf.w, clf.b,1)
        pl.plot([a0, b0],[a1, b1], "k--")

        a0=-4; a1=f(a0, clf.w, clf.b,-1)
        b0=4;b1=f(bo, clf.w, clf.b,-1)
        pl.plot([a0, b0],[a1, b1], "k--")

        pl.axis("tight")
        pl.show()

    def plot_contour(X1_train, X2_train, clf):
        pl.plot(X1_train[:,0], X1_train[:,1], "ro")
        pl.plot(X2_train[:,0], X2_train[:,1], "bo")
        pl.scatter(clf.sv[:,0], clf.sv[:,1], s=100, c="g")

        X1, X2 = np.meshgrig(np.linspace(-6, 6, 50), np.linspace(-6,6,50))
        X=np.array([[x1, x2] for x1, x2 in zip(np.ravel(X1), np.ravel(X2))])
        Z=clf.project(X) .reshape(X1.shape)

        pl.contour(X1, X2, Z, [0.0], colors='k', linewidths=1, origin='lower')
        pl.contour(X1, X2, Z+1, [0.0], colors='grey', linewidths=1, origin='lower')
        pl.contour(X1, X2, Z-1, [0.0], colors='grey', linewidths=1, origin='lower')

        pl.axis("tight")
        pl.show
    def test_linear():
        X1, y1, X2, y2=gen_lin_separable_data()
        X_train, y_train=split_train(X1, y1, X2, y2)
        X_test, y_test=split_test(X1, y1, X2, y2)

        clf=SVM()
        clf.fit(X_train, y_train)

        y_predict=clf.predict(X_test)
        correct=np.sum(y_predict==y_test)
        print("%d out of %d predictions correct" % (correct, len(y_predict)))
        plot_margin(X_train[y_train==1], X_train[y_train==-1], clf)

    def test_non_linear():
        X1, y1, X2, y2=gen_non_lin_separable_data()
        X_train, y_train=split_train(X1, y1, X2, y2)
        X_test, y_test=split_test(X1, y1, X2, y2)

        clf=SVM(polynominal_kernel)
        clf.fit(X_train, y_train)

        y_predict=clf.predict(X_test)
        correct=np.sum(y_predict==y_test)
        print("%d out of %d predictions correct" % (correct, len(y_predict)))
        plot_contour(X_train[y_train==1], X_train[y_train==-1], clf)

    def test_soft():
        X1, y1, X2, y2=gen_lin_separable_overlap_data()
        X_train, y_train=split_train(X1, y1, X2, y2)
        X_test, y_test=split_test(X1, y1, X2, y2)

        clf=SVM(C=1000.1)
        clf.fit(X_train, y_train)

        y_predict=clf.predict(X_test)
        correct=np.sum(y_predict==y_test)
        print("%d out of %d predictions correct" % (correct, len(y_predict)))
        plot_contour(X_train[y_train==1], X_train[y_train==-1], clf)

# test_linear()
# test_non_linear()
# test_soft()

test_soft()

Answer 1

我的错误是：if __name__=="main":，应该是if __name__=="__main__":