@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')