python支持向量机回归_支持向量机回归的Scikitlearn网格搜索

我正在学习交叉验证网格搜索，并遇到了这个youtube playlist，教程也作为ipython笔记本上传到了github。我试图在同时搜索多个参数部分重新创建代码，但我使用的不是knn，而是支持向量机回归。这是我的密码from sklearn.datasets import load_iris

from sklearn import svm

from sklearn.grid_search import GridSearchCV

import matplotlib.pyplot as plt

import numpy as np

iris = load_iris()

X = iris.data

y = iris.target

k=['rbf', 'linear','poly','sigmoid','precomputed']

c= range(1,100)

g=np.arange(1e-4,1e-2,0.0001)

g=g.tolist()

param_grid=dict(kernel=k, C=c, gamma=g)

print param_grid

svr=svm.SVC()

grid = GridSearchCV(svr, param_grid, cv=5,scoring='accuracy')

grid.fit(X, y)

print()

print("Grid scores on development set:")

print()

print grid.grid_scores_

print("Best parameters set found on development set:")

print()

print(grid.best_params_)

print("Grid best score:")

print()

print (grid.best_score_)

# create a list of the mean scores only

grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]

print grid_mean_scores

但它给了这个错误raise ValueError("X should be a square kernel matrix") ValueError: X

should be a square kernel matrix