TUKRの実装@德永

Lecture_TUKR/tokunaga/data.py

@@ -2,39 +2,65 @@
 import matplotlib.pyplot as plt
 
 def load_kura_tsom(xsamples, ysamples, missing_rate=None,retz=None):
-    # z1 = np.linspace(-1, 1, xsamples)
-    # z2 = np.linspace(-1, 1, ysamples)
+    z1 = np.linspace(-1, 1, xsamples)
+    z2 = np.linspace(-1, 1, ysamples)
 
-    z1 = np.random.uniform(-1, 1, xsamples)
-    z2 = np.random.uniform(-1, 1, ysamples)
+    #z1 = np.random.uniform(-1, 1, xsamples)
+    #z2 = np.random.uniform(-1, 1, ysamples)
 
     z1_repeated, z2_repeated = np.meshgrid(z1, z2, indexing='ij')
     x1 = z1_repeated
     x2 = z2_repeated
-    x3 = x1**2 - x2**2 + + np.random.uniform(-0.5, 0.5, xsamples*ysamples).reshape(xsamples, ysamples)
+    x3 = 1 * (x1**2 - x2**2) + + np.random.uniform(-0.5, 0.5, xsamples*ysamples).reshape(xsamples, ysamples)
     #ノイズを加えたい時はここをいじる,locがガウス分布の平均、scaleが分散,size何個ノイズを作るか
     #このノイズを加えることによって三次元空間のデータ点は上下に動く
 
     x = np.concatenate([x1[:, :, None], x2[:, :, None], x3[:, :, None]], axis=2)
-    truez = np.concatenate([x1[:, :, None], x2[:, :, None]], axis=2)
+    truez = np.concatenate([x1[:, None], x2[:, None]], axis=2)
 
     if missing_rate == 0 or missing_rate == None:
         if retz:
             return x, truez
         else:
             return x
 
+def load_kura_list(xsamples, ysamples, missing_rate=None, retz=None):
+    z1 = np.random.uniform(-1, 1, xsamples)
+    z2 = np.random.uniform(-1, 1, ysamples)
+    z1_num = np.arange(xsamples)
+    z2_num = np.arange(ysamples)
+
+    z1_repeated, z2_repeated = np.meshgrid(z1, z2)
+    z1_num_repeated, z2_num_repeated = np.meshgrid(z1_num, z2_num)
+    x1 = z1_repeated.reshape(-1)
+    x2 = z2_repeated.reshape(-1)
+    x1_num = z1_num_repeated.reshape(-1)
+    x2_num = z2_num_repeated.reshape(-1)
+    x3 = x1**2 - x2**2 + np.random.uniform(-0.5, 0.5, xsamples*ysamples)
+    #ノイズを加えたい時はここをいじる,locがガウス分布の平均、scaleが分散,size何個ノイズを作るか
+    #このノイズを加えることによって三次元空間のデータ点は上下に動く
+
+    x = np.concatenate([x1[:, None], x2[:, None], x3[:, None]], axis=1)
+    x_num = np.concatenate([x1_num[:, None], x2_num[:, None]], axis=1)
+
+    if missing_rate == 0 or missing_rate == None:
+        if retz:
+            return x
+        else:
+            return x, x_num
+
 if __name__ == '__main__':
     import matplotlib.pyplot as plt
     from mpl_toolkits.mplot3d import Axes3D
 
     xsamples = 10
     ysamples = 15
 
-    x, truez = load_kura_tsom(xsamples, ysamples)
-    print(x.shape)
+    #x = load_kura_tsom(xsamples, ysamples)
+    x = load_kura_list(xsamples, ysamples)
+    #print(plt.rcParams['image.cmap'])
     fig = plt.figure(figsize=[5, 5])
     ax_x = fig.add_subplot(projection='3d')
-    ax_x.scatter(x[:, :, 0], x[:, :, 1], x[:, :, 2])
+    ax_x.scatter(x[:, 0], x[:, 1], x[:, 2])
     ax_x.set_title('Generated three-dimensional data')
     plt.show()