TUKR実装-福永

Lecture_TUKR/TUKR.py

@@ -0,0 +1,187 @@
+import numpy as np
+
+from tqdm import tqdm #プログレスバーを表示させてくれる
+import jax,jaxlib
+import jax.numpy as jnp
+
+
+class UKR:
+    def __init__(self, X, latent_dim, sigma1, sigma2, prior='random', Uinit=None, Vinit=None):
+        #--------初期値を設定する．---------
+        self.X = X
+        #ここから下は書き換えてね
+        self.xsamples = X.shape[0]
+        self.ysamples = X.shape[1]
+        self.ob_dim = X.shape[2]
+        self.sigma1 = sigma1
+        self.sigma2 = sigma2
+        self.latent_dim = latent_dim
+
+
+        if Uinit is None:
+            if prior == 'random': #一様事前分布のとき
+                self.U = np.random.uniform(low=-0.001, high=0.001, size=(self.xsamples, self.latent_dim))
+
+
+            else: #ガウス事前分布のとき
+                self.U = np.random.normal(0, self.sigma1, (self.xsamples, self.latent_dim))
+
+        else: #Zの初期値が与えられた時
+            self.U = Uinit
+        self.history = {}
+        if Vinit is None:
+            if prior == 'random': #一様事前分布のとき
+                self.V = np.random.uniform(low=-0.001, high=0.001, size=(self.ysamples, self.latent_dim))
+
+            else: #ガウス事前分布のとき
+                self.V = np.random.normal(self.ysamples*self.latent_dim).reshape(self.ysamples, self.latent_dim)
+        else: #Zの初期値が与えられた時
+            self.V = Vinit
+
+        self.history = {}
+    def f(self, U, V):
+        Ud = np.sum((U[:, None, :] - self.U[None, :, :])**2, axis=2)
+        UH = -1*(Ud/(2*self.sigma1**2))
+        Uh = jnp.exp(UH)
+
+        Vd = np.sum((V[:, None, :] - self.V[None, :, :]) ** 2, axis=2)
+        VH = -1 * (Vd / (2 * self.sigma2 ** 2))
+        Vh = jnp.exp(VH)
+
+        bunshi = jnp.einsum('KI,MJ,IJD -> KMD', Uh, Vh, self.X)
+        # aaa = jnp.einsum('MJ,IJD -> IMD', Vh, self.X)
+        # bunshi = jnp.einsum('KI,IMD -> KMD', Uh, aaa)
+
+        bunbo = jnp.einsum('KI,MJ -> KM', Uh, Vh)
+        newbunbo = bunbo[:, :, None]
+
+
+
+        f = bunshi/newbunbo
+        # print(U.shape)
+        #写像の計算
+        return f
+    def ff(self, U, V, epoch):
+        Ud = np.sum((U[:, None, :] - self.history['u'][epoch][None, :, :]) ** 2, axis=2)
+        UH = -1 * (Ud / (2 * self.sigma1 ** 2))
+        Uh = jnp.exp(UH)
+
+        Vd = np.sum((V[:, None, :] - self.history['v'][epoch][None, :, :]) ** 2, axis=2)
+        VH = -1 * (Vd / (2 * self.sigma2 ** 2))
+        Vh = jnp.exp(VH)
+
+        bunshi = jnp.einsum('KI,MJ,IJD -> KMD', Uh, Vh, self.X)
+        # aaa = jnp.einsum('MJ,IJD -> IMD', Vh, self.X)
+        # bunshi = jnp.einsum('KI,IMD -> KMD', Uh, aaa)
+
+        bunbo = jnp.einsum('KI,MJ -> KM', Uh, Vh)
+        newbunbo = bunbo[:, :, None]
+
+        return bunshi/newbunbo
+
+    def E(self, U, V,  X, alpha, norm): #目的関数の計算
+        E = jnp.sum((X - self.f(U, V))**2)
+        UR = alpha * jnp.sum(jnp.abs(U ** norm))
+        VR = alpha * jnp.sum(jnp.abs(V ** norm))
+        E = E/self.xsamples/self.ysamples + UR/self.xsamples + VR/self.ysamples
+
+        return E
+
+
+
+    def fit(self, nb_epoch: int, eta: float, alpha: float, norm: float):
+        # 学習過程記録用
+        self.history['u'] = np.zeros((nb_epoch, self.xsamples, self.latent_dim))
+        self.history['v'] = np.zeros((nb_epoch, self.ysamples, self.latent_dim))
+        self.history['f'] = np.zeros((nb_epoch, self.xsamples, self.ysamples, self.ob_dim))
+        self.history['error'] = np.zeros(nb_epoch)
+
+        for epoch in tqdm(np.arange(nb_epoch)):
+            dEdu = jax.grad(self.E, argnums=0)(self.U, self.V, self.X, alpha, norm)
+            self.U = self.U - eta * dEdu
+            dEdv = jax.grad(self.E, argnums=1)(self.U, self.V, self.X, alpha, norm)
+            self.V = self.V - eta * dEdv
+
+           # U,Vの更新
+
+
+
+
+            # 学習過程記録用
+            self.history['u'][epoch] = self.U
+            self.history['v'][epoch] = self.V
+            self.history['f'][epoch] = self.f(self.U, self.V)
+            self.history['error'][epoch] = self.E(self.U, self.V, self.X, alpha, norm)
+
+    #--------------以下描画用(上の部分が実装できたら実装してね)---------------------
+    def calc_approximate_fu(self, resolution): #fのメッシュ描画用，resolution:一辺の代表点の数
+        nb_epoch = self.history['u'].shape[0]
+
+        self.history['y'] = np.zeros((nb_epoch, self.xsamples, self.ysamples, self.ob_dim))
+        for epoch in range(nb_epoch):
+            uzeta = self.create_uzeta(self.history['u'][epoch])
+            vzeta = self.create_vzeta(self.history['v'][epoch])
+            Y = self.ff(uzeta, vzeta, epoch)
+            # print(self.history['y'][epoch])
+            self.history['y'][epoch] = Y
+
+
+
+    def create_uzeta(self, Z): #fのメッシュの描画用に潜在空間に代表点zetaを作る．
+
+        u_y = np.linspace(np.amin(Z), np.amax(Z), self.xsamples).reshape(-1, 1)
+        # UYY = np.meshgrid(u_y)
+        # A = np.amin(Z)
+        # print(self.U.shape)
+
+        # zeta = np.concatenate([uxx[:, None], uyy[:, None]], axis=1)
+        return u_y
+
+    def create_vzeta(self, Z): #fのメッシュの描画用に潜在空間に代表点zetaを作る．
+
+        v_y = np.linspace(np.min(Z), np.max(Z), self.ysamples).reshape(-1, 1)
+        # UYY = np.meshgrid(u_y)
+
+
+        # zeta = np.concatenate([uxx[:, None], uyy[:, None]], axis=1)
+        return v_y
+
+
+
+if __name__ == '__main__':
+    from Lecture_UKR.data import create_kura
+    from Lecture_UKR.data import create_rasen
+    # from Lecture_UKR.data import create_2d_sin_curve
+    from Lecture_TUKR.visualizer import visualize_history
+    from Lecture_TUKR.data_scratch import load_kura_tsom
+    from Lecture_TUKR.animal import load_date
+
+    #各種パラメータ変えて遊んでみてね．
+    epoch = 200 #学習回数
+    sigma1 = 0.1 #カーネルの幅
+    sigma2 = 0.1
+    eta = 8 #学習率
+    latent_dim = 2 #潜在空間の次元
+    alpha = 0.01
+    norm = 10
+    seed = 4
+    np.random.seed(seed)
+
+    #入力データ（詳しくはdata.pyを除いてみると良い）
+    xsamples = 20 #データ数
+    ysamples = 10
+    # X = load_kura_tsom(xsamples, ysamples) #鞍型データ　ob_dim=3, 真のL=2
+    X = load_date()[0][:, :, None]
+    animal_label = load_date(retlabel_animal=True)[1]
+    feature_label = load_date(retlabel_feature=True)[2]
+
+    #X = create_rasen(nb_samples) #らせん型データ　ob_dim=3, 真のL=1
+    # X = create_2d_sin_curve(nb_samples) #sin型データ　ob_dim=2, 真のL=1
+    ukr = UKR(X, latent_dim, sigma1, sigma2, prior='random')
+    ukr.fit(epoch, eta, alpha, norm)
+    #visualize_history(X, ukr.history['f'], ukr.history['z'], ukr.history['error'], save_gif=False, filename="tmp")
+
+    #----------描画部分が実装されたらコメントアウト外す----------
+    ukr.calc_approximate_fu(resolution=10)
+    # ukr.calc_approximate_fv(resolution=10)
+    visualize_history(X, ukr.history['y'], ukr.history['u'], ukr.history['v'], ukr.history['error'], save_gif=True, filename="/Users/furukawashuushi/Desktop/3ヶ月コースGIF/TUKR動物", label1=animal_label, label2=feature_label)

Lecture_TUKR/animal.py

@@ -0,0 +1,28 @@
+import numpy as np
+import os
+
+
+def load_date(retlabel_animal=True, retlabel_feature=True):
+    datestore_name = 'datestore/animal'
+    file_name = 'features.txt'
+
+    directory_path = os.path.join(os.path.dirname(__file__), datestore_name)
+    file_path = os.path.join(directory_path, file_name)
+
+    x = np.loadtxt(file_path)
+
+    return_objects = [x]
+
+    if retlabel_animal:
+        label_name = 'labels_animal.txt'
+        label_path =os.path.join(directory_path, label_name)
+        label_animal =np.genfromtxt(label_path, dtype=str)
+        return_objects.append(label_animal)
+
+    if retlabel_feature:
+        label_name ='labels_feature.txt'
+        label_path =os.path.join(directory_path, label_name)
+        label_feature =np.genfromtxt(label_path, dtype=str)
+        return_objects.append(label_feature)
+
+    return return_objects

Lecture_TUKR/data_scratch.py

@@ -2,19 +2,18 @@
 import matplotlib.pyplot as plt
 
 def load_kura_tsom(xsamples, ysamples, missing_rate=None,retz=False):
-    z1 = 
-    z2 = 
+    z1 = np.random.normal(0, 0.5, xsamples)
+    z2 = np.random.normal(0, 0.5, ysamples)
 
-    z1_repeated, z2_repeated = 
-    x1 = 
-    x2 = 
-    x3 = 
+    z1_repeated, z2_repeated = np.meshgrid(z1, z2, indexing = 'ij')
+    x1 = z1_repeated #+ np.random.normal(loc=0.0, scale=0.1, size=(xsamples, ysamples))
+    x2 = z2_repeated #+ np.random.normal(loc=0.0, scale=0.1, size=(xsamples, ysamples))
+    x3 = x1**2 - x2**2
     #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 = 
-    truez = 
-
+    x = np.concatenate((x1[:, :, np.newaxis],x2[:, :, np.newaxis], x3[:, :, np.newaxis]), axis=2)
+    truez = np.concatenate((x1[:, :, np.newaxis],x2[:, :, np.newaxis], x3[:, :, np.newaxis]), axis=2)
     if missing_rate == 0 or missing_rate == None:
         if retz:
             return x, truez
@@ -25,13 +24,13 @@ def load_kura_tsom(xsamples, ysamples, missing_rate=None,retz=False):
     import matplotlib.pyplot as plt
     from mpl_toolkits.mplot3d import Axes3D
 
-    xsamples = 
-    ysamples = 
+    xsamples = 10
+    ysamples = 5
+
+    x, truez = load_kura_tsom(xsamples, ysamples, retz=True)
 
-    x, truez = load_kura_tsom() 
-
     fig = plt.figure(figsize=[5, 5])
     ax_x = fig.add_subplot(projection='3d')
-    ax_x.scatter()
+    ax_x.scatter(x[:, :, 0].flatten(), x[:, :, 1].flatten(), x[:, :, 2].flatten())
     ax_x.set_title('Generated three-dimensional data')
     plt.show()

Lecture_TUKR/datestore/animal/features.txt

@@ -0,0 +1,17 @@
+1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.5 0.5 0.0 0.0 0.0
+0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.5 0.5 0.0 0.0 0.0
+0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.5 0.5 0.0 0.0 0.0
+0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.5 0.5 0.0 0.0 0.0
+0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.3 0.0 0.0 1.0 1.0 0.0 0.5 0.5 0.0 0.0 0.0
+0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
+0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
+0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
+0.0 1.0 0.0 0.5 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.3 0.7 1.0 0.0 0.0
+0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0
+0.0 1.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0
+1.0 0.0 0.0 0.5 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0
+0.0 0.0 1.0 0.5 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
+0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0
+0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0
+0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
+0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0

Lecture_TUKR/datestore/animal/labels_animal.txt

@@ -0,0 +1,17 @@
+dove
+cock
+duck
+w_duck
+owl
+hawk
+eagle
+crow
+fox
+dog
+wolf
+cat
+tiger
+lion
+horse
+zebra
+cattle

Lecture_TUKR/datestore/animal/labels_feature.txt

@@ -0,0 +1,21 @@
+small
+medium
+large
+nocturnality
+two_legs
+four_legs
+hair
+hoof
+mane
+wing
+stripe
+hunt
+run
+fly
+swim
+domestic
+herbivorous
+carnivore
+canidae
+felidae
+pet