máquina de apoyo al aprendizaje pitón de máquinas de vectores de regresión no lineal modelo SVR

En este artículo se describe la máquina de vectores de soporte pitón no lineal modelo SVR regresión, preámbulos, como sigue:

import numpy as np
import matplotlib.pyplot as plt
 
from sklearn import datasets, linear_model,svm
from sklearn.model_selection import train_test_split
 
def load_data_regression():
  '''
  加载用于回归问题的数据集
  '''
  diabetes = datasets.load_diabetes() #使用 scikit-learn 自带的一个糖尿病病人的数据集
  # 拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4
  return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)
 
#支持向量机非线性回归SVR模型
def test_SVR_linear(*data):
  X_train,X_test,y_train,y_test=data
  regr=svm.SVR(kernel='linear')
  regr.fit(X_train,y_train)
  print('Coefficients:%s, intercept %s'%(regr.coef_,regr.intercept_))
  print('Score: %.2f' % regr.score(X_test, y_test))
   
# 生成用于回归问题的数据集
X_train,X_test,y_train,y_test=load_data_regression() 
# 调用 test_LinearSVR
test_SVR_linear(X_train,X_test,y_train,y_test)

def

test_SVR_poly(*data):
  '''
  测试 多项式核的 SVR 的预测性能随 degree、gamma、coef0 的影响.
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()
  ### 测试 degree ####
  degrees=range(1,20)
  train_scores=[]
  test_scores=[]
  for degree in degrees:
    regr=svm.SVR(kernel='poly',degree=degree,coef0=1)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,3,1)
  ax.plot(degrees,train_scores,label="Training score ",marker='+' )
  ax.plot(degrees,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_poly_degree r=1")
  ax.set_xlabel("p")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1.)
  ax.legend(loc="best",framealpha=0.5)
 
  ### 测试 gamma，固定 degree为3， coef0 为 1 ####
  gammas=range(1,40)
  train_scores=[]
  test_scores=[]
  for gamma in gammas:
    regr=svm.SVR(kernel='poly',gamma=gamma,degree=3,coef0=1)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,3,2)
  ax.plot(gammas,train_scores,label="Training score ",marker='+' )
  ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_poly_gamma r=1")
  ax.set_xlabel(r"$\gamma$")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  ### 测试 r，固定 gamma 为 20，degree为 3 ######
  rs=range(0,20)
  train_scores=[]
  test_scores=[]
  for r in rs:
    regr=svm.SVR(kernel='poly',gamma=20,degree=3,coef0=r)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,3,3)
  ax.plot(rs,train_scores,label="Training score ",marker='+' )
  ax.plot(rs,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_poly_r gamma=20 degree=3")
  ax.set_xlabel(r"r")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1.)
  ax.legend(loc="best",framealpha=0.5)
  plt.show()
   
# 调用 test_SVR_poly
test_SVR_poly(X_train,X_test,y_train,y_test)

def test_SVR_rbf(*data):
  '''
  测试 高斯核的 SVR 的预测性能随 gamma 参数的影响
  '''
  X_train,X_test,y_train,y_test=data
  gammas=range(1,20)
  train_scores=[]
  test_scores=[]
  for gamma in gammas:
    regr=svm.SVR(kernel='rbf',gamma=gamma)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  fig=plt.figure()
  ax=fig.add_subplot(1,1,1)
  ax.plot(gammas,train_scores,label="Training score ",marker='+' )
  ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_rbf")
  ax.set_xlabel(r"$\gamma$")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  plt.show()
   
# 调用 test_SVR_rbf
test_SVR_rbf(X_train,X_test,y_train,y_test)

def test_SVR_sigmoid(*data):
  '''
  测试 sigmoid 核的 SVR 的预测性能随 gamma、coef0 的影响.
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()
 
  ### 测试 gammam，固定 coef0 为 0.01 ####
  gammas=np.logspace(-1,3)
  train_scores=[]
  test_scores=[]
 
  for gamma in gammas:
    regr=svm.SVR(kernel='sigmoid',gamma=gamma,coef0=0.01)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,2,1)
  ax.plot(gammas,train_scores,label="Training score ",marker='+' )
  ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_sigmoid_gamma r=0.01")
  ax.set_xscale("log")
  ax.set_xlabel(r"$\gamma$")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  ### 测试 r ，固定 gamma 为 10 ######
  rs=np.linspace(0,5)
  train_scores=[]
  test_scores=[]
 
  for r in rs:
    regr=svm.SVR(kernel='sigmoid',coef0=r,gamma=10)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,2,2)
  ax.plot(rs,train_scores,label="Training score ",marker='+' )
  ax.plot(rs,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_sigmoid_r gamma=10")
  ax.set_xlabel(r"r")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  plt.show()
   
# 调用 test_SVR_sigmoid
test_SVR_sigmoid(X_train,X_test,y_train,y_test)
def test_SVR_sigmoid(*data):
  '''
  测试 sigmoid 核的 SVR 的预测性能随 gamma、coef0 的影响.
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()

  ### 测试 gammam，固定 coef0 为 0.01 ####
  gammas=np.logspace(-1,3)
  train_scores=[]
  test_scores=[]

  for gamma in gammas:
    regr=svm.SVR(kernel='sigmoid',gamma=gamma,coef0=0.01)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,2,1)
  ax.plot(gammas,train_scores,label="Training score ",marker='+' )
  ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_sigmoid_gamma r=0.01")
  ax.set_xscale("log")
  ax.set_xlabel(r"$\gamma$")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  ### 测试 r ，固定 gamma 为 10 ######
  rs=np.linspace(0,5)
  train_scores=[]
  test_scores=[]

  for r in rs:
    regr=svm.SVR(kernel='sigmoid',coef0=r,gamma=10)
    regr.fit(X_train,y_train)
    train_scores.append(regr.score(X_train,y_train))
    test_scores.append(regr.score(X_test, y_test))
  ax=fig.add_subplot(1,2,2)
  ax.plot(rs,train_scores,label="Training score ",marker='+' )
  ax.plot(rs,test_scores,label= " Testing score ",marker='o' )
  ax.set_title( "SVR_sigmoid_r gamma=10")
  ax.set_xlabel(r"r")
  ax.set_ylabel("score")
  ax.set_ylim(-1,1)
  ax.legend(loc="best",framealpha=0.5)
  plt.show()
  
# 调用 test_SVR_sigmoid
test_SVR_sigmoid(X_train,X_test,y_train,y_test)

Por último, se recomienda una muy amplia recolección de recursos de aprendizaje pitón, [haga clic para entrar] , aquí están mi colección antes de la experiencia, notas de estudio el aprendizaje, hay una posibilidad de experiencia en los negocios, y se calmó a cero sobre la base de información para combatir proyecto , podemos en la parte inferior, dejar un mensaje, no sé a presentar, vamos a estudiar juntos el progreso