pca

CH16/pca.py

@@ -13,15 +13,30 @@ def __init__(self, n_components=2):
         self.n_components_ = n_components
         self.explained_variance_ratio_ = None
         self.singular_values_ = None
+        self.u = None
+        self.vh = None
+        self.components_ = None
 
     def __str__(self,):
         rst = "PCA algorithms:\n"
         rst += "n_components: " + str(self.n_components_)
         return rst
-    
+
     def fit(self, x):
         # check n_components and min(n_samples, n_features)
-        pass
-
-    def fit_transform(x):
-        return x
+        n = x.shape[0]
+        assert n > 1
+        assert (np.mean(x, axis=1) == np.zeros(n)).all()
+        x_ = x.T/np.sqrt(n-1)
+        u, s, vh = np.linalg.svd(x_)
+        self.vh = vh
+        self.u = u
+        self.singular_values_ = s
+        self.explained_variance_ratio_ = s**2/np.sum(s**2)
+        print(self.u)
+        print(self.vh)
+
+    def fit_transform(self, x):
+        self.fit(x)
+        self.components_ = np.dot(self.vh, x)
+        return self.components_

CH16/unit_test.py

@@ -111,20 +111,34 @@ def test_ex1601(self):
         # plt.show()
 
     def test_pca(self):
+        """
+
+        PCA分析
+        """
+        print("\n")
         # raw data
         x = np.array([[2, 3, 3, 4, 5, 7],
                       [2, 4, 5, 5, 6, 8]])
+        x = x-np.mean(x, axis=1).reshape(-1, 1)
+        print(x)
+        assert (np.mean(x, axis=1) == np.zeros(2)).all()
+
         # for sklearn x.shape == (n_samples, n_features)
         pca_sklearn = skpca(n_components=2)
         pca_sklearn.fit(x.T)
+
         print("\n")
         print(40*"*"+"sklearn_pca"+40*"*")
-        print(pca_sklearn.explained_variance_ratio_)
         print(pca_sklearn.singular_values_)
+        print(pca_sklearn.explained_variance_ratio_)
+        print(pca_sklearn.fit_transform(x.T).T)
 
         print(40*"*"+"smirk_pca"+40*"*")
         pca_test = smirkpca(n_components=2)
-        print(pca_test)
+        rst = pca_test.fit_transform(x)
+        print(pca_test.singular_values_)
+        print(pca_test.explained_variance_ratio_)
+        print(rst)
 
     def test_pca_get_fig(self):
         pass