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q01_k_means/build.py

@@ -1,6 +1,12 @@
 # Default imports
 from sklearn.cluster import KMeans
+import matplotlib
+matplotlib.use('Agg')
+matplotlib.rcParams['interactive'] == True
+
 import matplotlib.pyplot as plt
+
+
 from sklearn import datasets
 
 
@@ -10,8 +16,35 @@
 y_train = digits.target
 
 # Write your solution here :
-
-
-
-
-
+def k_means ( X_train, y_train, cluster = 10,random_state = 9):
+    model = KMeans(init="k-means++", n_clusters=cluster,random_state=random_state )
+    nsamples, nx, ny = X_train.shape
+    X_train = X_train.reshape((nsamples,nx*ny))
+    labels = model.fit_predict ( X_train )
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=labels, s=50, cmap='viridis')
+    return
+
+def k_means_ga(X_train, y_train, cluster=10, random_state=9):
+    X = X_train.reshape((len(X_train), -1))
+    kmeans = KMeans(n_clusters=cluster, random_state=random_state).fit(X, y_train)
+    a = X_train[(y_train == 0) & (kmeans.labels_ == 0)][0:20]
+    b = X_train[(y_train == 1) & (kmeans.labels_ == 1)][0:20]
+    c = X_train[(y_train == 2) & (kmeans.labels_ == 2)][0:20]
+    d = X_train[(y_train == 3) & (kmeans.labels_ == 3)][0:20]
+    e = X_train[(y_train == 4) & (kmeans.labels_ == 4)][0:20]
+    f = X_train[(y_train == 5) & (kmeans.labels_ == 5)][0:20]
+    g = X_train[(y_train == 6) & (kmeans.labels_ == 6)][0:20]
+    h = X_train[(y_train == 7) & (kmeans.labels_ == 7)][0:20]
+    i = X_train[(y_train == 8) & (kmeans.labels_ == 8)][0:20]
+    j = X_train[(y_train == 9) & (kmeans.labels_ == 9)][0:20]
+    for char in (a, b, c, d, e, f, g, h, i, j):
+        for index in range(0, len(char)):
+            plt.subplot(10, 20, index + 1)
+            plt.axis('off')
+            plt.imshow(char[index])
+        plt.show()
+
+
+k_means ( X_train, y_train)
+#plt.savefig('D:\\output.pdf', format='pdf', dpi=1200)
+plt.show()