📝 PCA主成分分析，数据降维

Author: lawlite19

readme.md

@@ -950,12 +950,12 @@ def runKMeans(X,initial_centroids,max_iters,plot_process):
  - ![$${X_{approx}} = {(U_{reduce}^{ - 1})^{ - 1}}Z = {U_{reduce}}Z$$](http://latex.codecogs.com/gif.latex?%5Cfn_cm%20%24%24%7BX_%7Bapprox%7D%7D%20%3D%20%7B%28U_%7Breduce%7D%5E%7B%20-%201%7D%29%5E%7B%20-%201%7D%7DZ%20%3D%20%7BU_%7Breduce%7D%7DZ%24%24)
  - 实现代码：
  ```
-     # 恢复数据
-    def recoverData(Z,U,K):
-        X_rec = np.zeros((Z.shape[0],U.shape[0]))
-        U_recude = U[:,0:K]
-        X_rec = np.dot(Z,np.transpose(U_recude))  # 还原数据（近似）
-        return X_rec
+ # 恢复数据
+def recoverData(Z,U,K):
+    X_rec = np.zeros((Z.shape[0],U.shape[0]))
+    U_recude = U[:,0:K]
+    X_rec = np.dot(Z,np.transpose(U_recude))  # 还原数据（近似）
+    return X_rec
  ```
 
 ### 6、主成分个数的选择（即要降的维度）