SVM实现没有正确分类

Question

我正在python中构建一个SVM。但是，我的实施正在产生错误的飞机。我认为它与我的参数（langrage multipliers）之间的关系很小，但我不确定。我想我正在进行凸优化。也许我的数据格式不正确。我的代码基于这些教程：http://tullo.ch/articles/svm-py/和http://www.mblondel.org/journal/2010/09/19/support-vector-machines-in-python/。

这是我的代码和输出：

import numpy
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import cvxopt
from matplotlib import cm
from cvxopt import matrix, solvers
from itertools import izip
#http://www.tristanfletcher.co.uk/SVM%20Explained.pdf

class SVM:
    def __init__(self,X,y):
        self.X = X
        self.y = y

    def findParameters(self,X,y):

        # min 1/2 x^T P x + q^T x
        #Ax = b
        #y's are answer vectors 
        #put in cvxopt 
       # 

        """P = cvxopt.matrix(np.outer(self.y,self.y)* self.gramMatrix())
        q = cvxopt.matrix((numpy.ones(len(self.y))).T)
        #G = 
        #h = 
        limits = np.asarray(self.y)
        A = cvxopt.matrix(limits.T)


        #genrates matrix of zzeros
        b = cvxopt.matrix(numpy.zeros(len(self.y)))
        # actually comp
        param = cvxopt.solvers.qp(P,q,G,h,A,b);"""
        n_samples, n_features = X.shape
        K = self.gramMatrix(X)
        P = cvxopt.matrix(np.outer(y, y) * K)
        q = cvxopt.matrix(-1 * np.ones(n_samples))
        Gtry = cvxopt.matrix(np.diag(np.ones(n_samples) * -1))
        htry = cvxopt.matrix(np.zeros(n_samples))


        A = cvxopt.matrix(y, (1, n_samples))
        b = cvxopt.matrix(0.0)

        param = cvxopt.solvers.qp(P, q, Gtry, htry, A, b)
        array = param['x']
        return array


    def WB_calculator(self,X,y):
        #calculates w vector
        yi = self.y
        X = np.asarray(X)
        y = np.asarray(y)
        important = self.findParameters(X,y)
        print("these are parameters")
        print(important)
        firstsum = [0 for x in range(0,len(y))]
        for point in range(0,len(important)):
            liste = X[point]*important[point]*yi[point]
            firstsum = [x + y for x, y in zip(firstsum,liste)]



        #this part calculates bias
            #this is a very naive implementation of bias
            #xstuff is the x_coordinate vector we find this by transpose
            b = 0
        for i in range(0,len(important)):
            b = b+ (yi[i]- np.dot(firstsum,X[i]))

        avgB = b/len(important)
        answer = (firstsum , avgB)
        print("w vector")
        print(firstsum)
        return answer



    def polynomialK(self,u,v,b):
        return (np.dot(u,v)+b)**2    

#Guassian Kernal Funciton 
    def gaussianK(self,v1, v2, sigma):
        return np.exp(-norm(v1-v2, 2)**2/(2.*sigma**2))

#computes the gramMatrix given a set of all points included in the data
#this is basicly a matrix of dot prodducts

    def gramMatrix(self,X): 
        gramMatrix = []
        data = np.asarray(self.X)
        dataTran = data
        #print(dataTran)
        for x in dataTran:
            row = []
            #print(row)
            for y in dataTran:

                row.append(np.dot(x,y))

            gramMatrix.append(row)
            #print(row)
        return gramMatrix
    def determineAcceptance(self,point,X,y):
        # I'm not sure if this is the proper bounding lets checl
        cutoff = self.WB_calculator(X,y)
        if(np.dot(cutoff[0],point)+cutoff[1] >0):
            print("You got in")
        elif(np.dot(cutoff[0],point)+cutoff[1]<0):
            print("Study")

    # plots  plane and points
    def Graph(self,X,y):
        important_stuff = self.WB_calculator(X,y)
        weights = important_stuff[0] 
        c = important_stuff[1]
        #here we actaually graph the functionb 
        graphable = X.T
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')
        xs = graphable[0]
        ys = graphable[1]
        zs = graphable[2]

        colors = self.y
        ax.scatter(xs,ys,zs,c=colors)
        ax.set_xlabel("A")
        ax.set_ylabel("B")
        ax.set_zlabel("C")
        #this changes orientation and look of surface
        ax.view_init(azim = 180+40,elev = 22)
        X = np.arange(-2, 2, 0.25)
        Y = np.arange(-2, 2, 0.25)
        X, Y = np.meshgrid(X, Y)

        Z = ((-weights[0]*X + -weights[1]*Y - c)/(weights[2]))
        surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
                       linewidth=0, antialiased=True)
        plt.show()


#list of points to test
a = [[-.1,-.1,-.1],[-.2,-.2,-.2],[.15,.15,.15],[.9,.9,.9],[.95,.95,.95]]
check = np.asarray(a)
b = [.01,.01,.01,1,1]
bigger =np.asarray(b)
d = SVM(a,b)
print(d.gramMatrix(check)[0])
print("parameters ya")
print(d.findParameters(check,bigger))
print(d.WB_calculator(check,bigger))
d.Graph(check,bigger) 
d.determineAcceptance([.01,.01,.01],check,bigger)