diff --git a/Chapter_16 PersonalRank/test.py b/Chapter_16 PersonalRank/test.py
new file mode 100644
index 0000000..e1af170
--- /dev/null
+++ b/Chapter_16 PersonalRank/test.py	
@@ -0,0 +1,84 @@
+# coding:utf-8
+
+from numpy import *
+
+def load_data(path):
+    f = open(path)
+    data = []
+    for line in f.readlines():
+        arr = []
+        lines = line.strip().split("\t")
+        for x in lines:
+            if x != "-":
+                arr.append(float(x))
+            else:
+                arr.append(float(0))
+        #print arr
+        data.append(arr)
+    #print data
+    return data
+
+def gradAscent(data, K):
+    dataMat = mat(data)
+    print(dataMat)
+    m, n = shape(dataMat)
+    p = mat(random.random((m, K)))
+    q = mat(random.random((K, n)))
+
+    alpha = 0.0002
+    beta = 0.02
+    maxCycles = 10000
+
+    for step in range(maxCycles):
+        for i in range(m):
+            for j in range(n):
+                if dataMat[i,j] > 0:
+                    #print dataMat[i,j]
+                    error = dataMat[i,j]
+                    for k in range(K):
+                        error = error - p[i,k]*q[k,j]
+                    for k in range(K):
+                        p[i,k] = p[i,k] + alpha * (2 * error * q[k,j] - beta * p[i,k])
+                        q[k,j] = q[k,j] + alpha * (2 * error * p[i,k] - beta * q[k,j])
+
+        # 损失函数，判断收敛
+        loss = 0.0
+        for i in range(m):
+            for j in range(n):
+                if dataMat[i,j] > 0:
+                    error = 0.0
+                    for k in range(K):
+                        error = error + p[i,k]*q[k,j]
+                    loss = (dataMat[i,j] - error) * (dataMat[i,j] - error)
+                    for k in range(K):
+                        loss = loss + beta * (p[i,k] * p[i,k] + q[k,j] * q[k,j]) / 2
+
+        if loss < 0.001:
+            break
+        # print(step)
+        if step % 1000 == 0:
+            print(loss)
+
+    return p, q
+
+
+if __name__ == "__main__":
+    dataMatrix = load_data("data.txt")
+
+    p, q = gradAscent(dataMatrix, 5)
+    '''
+    p = mat(ones((4,10)))
+    print p
+    q = mat(ones((10,5)))
+    '''
+    result = p * q
+    #print p
+    #print q
+
+    print(result)
+
+# [[ 4.01235942  2.99635324  2.8688891   4.96017332  4.30307713]
+#  [ 4.94058713  5.42013643  4.00107426  4.02193661  4.45573454]
+#  [ 3.9898181   4.46104338  4.97200637  3.48454365  3.00227572]
+#  [ 2.05763283  2.95284794  2.13460563  0.97376823  1.48549247]
+#  [ 4.50701719  3.99370117  2.00291634  4.50752955  4.97625932]]
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