added prediction code, need to perform minimization

model.py

@@ -5,12 +5,21 @@ import csv
 from random import random
 from pprint import pprint as pp
 from math import log, exp
-from functools import reduce
 import numpy as np
 
 params = {"layers":[57, int(57/2), 1], "test":"TestX.csv", "train":"Train.csv"}
 
-def getData(srcF, isTrain=True, addBias=True):
+def doNormalize(X):
+    #do 0 mean 1 std normalization
+    x1 = np.array(X,dtype=float)
+    for i in range(len(X[0])):
+        col = x1[:,i]
+        mean,std = col.mean(),col.std()
+        std = std if std!=0.0 else 1.0
+        x1[:,i] = (x1[:,i]-mean)/std
+    return x1.tolist()
+
+def getData(srcF, isTrain=True, addBias=True, normalize=True):
     X,y=[],[]
     with open(srcF) as src:
         reader = csv.reader(src, delimiter=',')
@@ -27,6 +36,9 @@ def getData(srcF, isTrain=True, addBias=True):
             if isTrain:
                 y.append(int(row[-1]))
 
+    if normalize:
+        X = doNormalize(X)
+    #print(X[0])
     return (X,y)
 
 def sigmoid(v):
@@ -87,10 +99,19 @@ def cost(li, lh, lo, weights, X, y, lamb):
     grad = gradient(tdel1, tdel2, w1.reshape((lh,li+1)), w2.reshape((lo,lh+1)), lamb, m)    
 
     return J,grad
+
+def predict(x, w1, w2):
+    x=[1]+x    #58x1
+    x = np.array(x)
+    h1 = sigmoid(np.dot(w1,x).tolist())    #28x58 * 58x1 = 28x1
+    h1 = [1]+h1
+    h2 = sigmoid(np.dot(w2,h1).tolist())    #1x29 * 29x1 = 1x1
+    return 1 if h2>0.5 else 0
+    
     
 if __name__ == "__main__":
     X,y = getData(params["train"])
-    # tX,ty = getData(params["test"], isTrain=False)
+    tX,ty = getData(params["test"], isTrain=False)
     # print(len(X), len(X[0]), len(y), X[0])
     # print(len(tX), len(ty), tX[0])
     li,lh,lo = tuple(params["layers"])