from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
# 重构的数据
x = np.array(lowDDataMat)[:,0]
y = np.array(lowDDataMat)[:,1]
plt.scatter(x,y,c='r')
plt.show()
predictions = mlp.predict(x_data)
# 重构的数据
x = np.array(lowDDataMat)[:,0]
y = np.array(lowDDataMat)[:,1]
plt.scatter(x,y,c=y_data)
plt.show()
lowDDataMat,reconMat = pca(x_data,3)
from mpl_toolkits.mplot3d import Axes3D
x = np.array(lowDDataMat)[:,0]
y = np.array(lowDDataMat)[:,1]
z = np.array(lowDDataMat)[:,2]
ax = plt.figure().add_subplot(111, projection ='3d')
ax.scatter(x, y, z, c = y_data, s =10)#点为红色三角形
plt.show()
.PCA算法-手写数字降维预测
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
from sklearn import decomposition
import matplotlib.pyplot as plt