TUKRの実装@德永

Lecture_TUKR/tokunaga/cross_test.py

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+#sampleフォルダのukr_torchとクロステストするプログラム
+
+import numpy as np
+from Lecture_UKR.tokunaga.ukr import UKR as ukr
+from Lecture_UKR.sample.ukr_torch import UKR as ukr_sample
+from Lecture_UKR.tokunaga.data import create_kura
+
+class TestUKR():
+    def test_UKR(self):
+        epoch = 100
+        sigma = 0.2
+        eta = 100
+        latent_dim = 2
+        nb_samples = 100
+        seed = 4
+        np.random.seed(seed)
+
+        X = create_kura(nb_samples)
+        Zinit = 0.2 * sigma * np.random.rand(nb_samples, latent_dim) - 0.1 * sigma
+
+        #サンプルプログラム
+        ukr_sam = ukr_sample(X, latent_dim, sigma, prior='random', Zinit=Zinit)
+        ukr_sam.fit(epoch, eta)
+
+        #作ったプログラム
+        ukr_mine = ukr(X, latent_dim, sigma, prior='random', Zinit=Zinit)
+        ukr_mine.fit(epoch, eta)
+
+        print("You are executing ", __file__)
+
+        np.testing.assert_allclose(
+            ukr_sam.history['z'],
+            ukr_mine.history['z'],
+            rtol=1e-05,
+            atol=1e-08)
+        np.testing.assert_allclose(
+            ukr_sam.history['f'],
+            ukr_mine.history['f'],
+            rtol=1e-05,
+            atol=1e-08)
+
+
+if __name__ == "__main__":
+    test_ukr = TestUKR()
+    test_ukr.test_UKR()
+    #unittest.main()

Lecture_TUKR/tokunaga/data.py

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+import numpy as np
+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.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 = 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)
+
+    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 = 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.set_title('Generated three-dimensional data')
+    plt.show()