Create nmf.py

Author: zhaozhiyong19890102

Chapter_15 MatrixFactorization/nmf.py

@@ -0,0 +1,61 @@
+# coding:UTF-8
+
+import numpy as np
+from mf import load_data, save_file, prediction, top_k
+
+def train(V, r, maxCycles, e):
+    m, n = np.shape(V)
+    # 1、初始化矩阵
+    W = np.mat(np.random.random((m, r)))
+    H = np.mat(np.random.random((r, n)))
+    
+    # 2、非负矩阵分解
+    for step in xrange(maxCycles):
+        V_pre = W * H
+        E = V - V_pre
+        err = 0.0
+        for i in xrange(m):
+            for j in xrange(n):
+                err += E[i, j] * E[i, j]
+
+        if err < e:
+            break
+        if step % 1000 == 0:
+            print "\titer: ", step, " loss: " , err
+
+        a = W.T * V
+        b = W.T * W * H
+        for i_1 in xrange(r):
+            for j_1 in xrange(n):
+                if b[i_1, j_1] != 0:
+                    H[i_1, j_1] = H[i_1, j_1] * a[i_1, j_1] / b[i_1, j_1]
+
+        c = V * H.T
+        d = W * H * H.T
+        for i_2 in xrange(m):
+            for j_2 in xrange(r):
+                if d[i_2, j_2] != 0:
+                    W[i_2, j_2] = W[i_2, j_2] * c[i_2, j_2] / d[i_2, j_2]
+
+    return W, H 
+
+
+if __name__ == "__main__":
+    # 1、导入用户商品矩阵
+    print "----------- 1、load data -----------"
+    V = load_data("data.txt")
+    # 2、非负矩阵分解
+    print "----------- 2、training -----------"    
+    W, H = train(V, 5, 10000, 1e-5)
+    # 3、保存分解后的结果
+    print "----------- 3、save decompose -----------"
+    save_file("W", W)
+    save_file("H", H)
+    # 4、预测
+    print "----------- 4、prediction -----------"
+    predict = prediction(V, W, H, 0)
+    # 进行Top-K推荐
+    print "----------- 5、top_k recommendation ------------"
+    top_recom = top_k(predict, 2)
+    print top_recom
+    print W * H