@author: Dawnborn
"""
# SVM
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#读取
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:, 4].values
#划分训练集测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
#特征量化
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transfrom(X_train)
X_test = sc.transform(X_test)
from sklearn.svm import SVC
clf = SVC(kern='linear', random_state=0)
clf.fit(X_train, y_train)
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
# Feature Scalling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transfrom(y)
# Fitting the SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)
# Fitting SVR to the dataset
# y_pred = regressor.predict(6.5)
y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(np.array([[6.5]]))
# Visuallizing the SVR results
plt.scatter(X, y, color = 'red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
