我使用“svm”分类器来区分是自行车还是汽车。因此，我的特征是第0、1、2列，依赖变量是第3列。我能够清楚地看到分类结果，但不知道如何根据分类在图表中打印所有点。
import numpy as np import operator from matplotlib import pyplot as plt from sklearn import svm from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.svm import SVC dataframe=pd.read_csv(DATASET_PATH) dataframe = dataframe.dropna(how='any',axis=0) SVM_Trained_Model = preprocessing.LabelEncoder() train_data=dataframe[0:len(dataframe)] le=preprocessing.LabelEncoder() col=dataframe.columns[START_TRAIN_COLUMN:].astype('U') col_name=["no_of_wheels","dimensions","windows","vehicle_type"] for i in range(0,len(col_name)): self.train_data[col_name[i]]=le.fit_transform(self.train_data[col_name[i]]) train_column=np.array(train_data[col]).astype('U') data=train_data.iloc[:,[0,1,2]].values target=train_data.iloc[:,3].values data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.30, random_state = 0) `split test and test train` svc_model=SVC(kernel='rbf', probability=True))'classifier model' svc_model.fit(data_train, target_train) all_labels =svc_model.predict(data_test) X_set, y_set = data_train, target_train X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(1)]).T pred = svc_model.predict(Xpred).reshape(X1.shape) plt.contourf(X1, X2, pred,alpha = 0.75, cmap = ListedColormap(('white','orange','pink'))) plt.xlim(X1.min(),X1.max()) plt.ylim(X2.min(), X2.max()) colors=['red','yellow','cyan','blue'] for i, j in enumerate(np.unique(y_set)): plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap((colors[i]))(i), label = j) plt.title('Multiclass Classifier ') plt.xlabel('Features') plt.ylabel('Dependents') plt.legend() plt.show()这是我的图表，我需要使用Python的print()函数根据图表中的粉红色和白色区域打印点。请帮助我获取这些点。
回答：
你需要选择并使用两个特征来制作一个2D表面图。
from sklearn.svm import SVCimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import svm, datasetsiris = datasets.load_iris()X = iris.data[:, :2] # we only take the first two features.y = iris.targetdef make_meshgrid(x, y, h=.02): x_min, x_max = x.min() - 1, x.max() + 1 y_min, y_max = y.min() - 1, y.max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) return xx, yydef plot_contours(ax, clf, xx, yy, **params): Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) out = ax.contourf(xx, yy, Z, **params) return outmodel = svm.SVC(kernel='linear')clf = model.fit(X, y)fig, ax = plt.subplots()# title for the plotstitle = ('Decision surface of linear SVC ')# Set-up grid for plotting.X0, X1 = X[:, 0], X[:, 1]xx, yy = make_meshgrid(X0, X1)plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')ax.set_ylabel('y label here')ax.set_xlabel('x label here')ax.set_xticks(())ax.set_yticks(())ax.set_title(title)ax.legend()plt.show()
