我正在用python构建一个SVM。然而,我的建议是生成错误的平面。我认为这与我的参数(langrage乘数)太小有关,但我不确定。我想我做的凸优化是对的。也许我的数据格式不对。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/。你知道吗
以下是我的代码和输出:
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)
目前没有回答
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