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mlassign2
Commit e53add22 authored by Sushant Mahajan's avatar Sushant Mahajan
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fixed normalization for first column and added parameter search for gradient descnet

parent 72cdc309
Pipeline #298 skipped
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Showing with 6180 additions and 163 deletions
Train2.csv 0 → 100644
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Trainweka.csv 0 → 100644
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answer.txt
View file @ e53add22
...@@ -5,14 +5,14 @@ Id,Label ...@@ -5,14 +5,14 @@ Id,Label
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...@@ -891,9 +891,9 @@ Id,Label ...@@ -891,9 +891,9 @@ Id,Label
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...@@ -918,7 +918,7 @@ Id,Label ...@@ -918,7 +918,7 @@ Id,Label
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...@@ -1015,12 +1015,12 @@ Id,Label ...@@ -1015,12 +1015,12 @@ Id,Label
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1024,0 1024,0
...@@ -1028,22 +1028,22 @@ Id,Label ...@@ -1028,22 +1028,22 @@ Id,Label
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...@@ -1069,7 +1069,7 @@ Id,Label ...@@ -1069,7 +1069,7 @@ Id,Label
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...@@ -1084,12 +1084,12 @@ Id,Label ...@@ -1084,12 +1084,12 @@ Id,Label
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...@@ -1102,7 +1102,7 @@ Id,Label ...@@ -1102,7 +1102,7 @@ Id,Label
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...@@ -1128,7 +1128,7 @@ Id,Label ...@@ -1128,7 +1128,7 @@ Id,Label
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...@@ -1136,9 +1136,9 @@ Id,Label ...@@ -1136,9 +1136,9 @@ Id,Label
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...@@ -1152,7 +1152,7 @@ Id,Label ...@@ -1152,7 +1152,7 @@ Id,Label
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...@@ -1165,7 +1165,7 @@ Id,Label ...@@ -1165,7 +1165,7 @@ Id,Label
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...@@ -1189,23 +1189,23 @@ Id,Label ...@@ -1189,23 +1189,23 @@ Id,Label
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...@@ -1214,9 +1214,9 @@ Id,Label ...@@ -1214,9 +1214,9 @@ Id,Label
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...@@ -1225,7 +1225,7 @@ Id,Label ...@@ -1225,7 +1225,7 @@ Id,Label
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...@@ -1246,8 +1246,8 @@ Id,Label ...@@ -1246,8 +1246,8 @@ Id,Label
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...@@ -1259,7 +1259,7 @@ Id,Label ...@@ -1259,7 +1259,7 @@ Id,Label
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...@@ -1269,7 +1269,7 @@ Id,Label ...@@ -1269,7 +1269,7 @@ Id,Label
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1314,0 1314,0
1315,0 1315,0
...@@ -1333,7 +1333,7 @@ Id,Label ...@@ -1333,7 +1333,7 @@ Id,Label
1331,0 1331,0
1332,0 1332,0
1333,0 1333,0
1334,0 1334,1
1335,0 1335,0
1336,1 1336,1
1337,0 1337,0
...@@ -1347,7 +1347,7 @@ Id,Label ...@@ -1347,7 +1347,7 @@ Id,Label
1345,0 1345,0
1346,0 1346,0
1347,1 1347,1
1348,1 1348,0
1349,1 1349,1
1350,1 1350,1
1351,1 1351,1
...@@ -1377,7 +1377,7 @@ Id,Label ...@@ -1377,7 +1377,7 @@ Id,Label
1375,0 1375,0
1376,1 1376,1
1377,0 1377,0
1378,1 1378,0
1379,0 1379,0
1380,0 1380,0
1381,0 1381,0
...@@ -1395,7 +1395,7 @@ Id,Label ...@@ -1395,7 +1395,7 @@ Id,Label
1393,0 1393,0
1394,0 1394,0
1395,0 1395,0
1396,1 1396,0
1397,0 1397,0
1398,0 1398,0
1399,1 1399,1
...@@ -1413,7 +1413,7 @@ Id,Label ...@@ -1413,7 +1413,7 @@ Id,Label
1411,1 1411,1
1412,0 1412,0
1413,1 1413,1
1414,0 1414,1
1415,0 1415,0
1416,1 1416,1
1417,1 1417,1
...@@ -1433,10 +1433,10 @@ Id,Label ...@@ -1433,10 +1433,10 @@ Id,Label
1431,1 1431,1
1432,0 1432,0
1433,0 1433,0
1434,1 1434,0
1435,0 1435,0
1436,1 1436,1
1437,1 1437,0
1438,1 1438,1
1439,1 1439,1
1440,0 1440,0
...@@ -1461,7 +1461,7 @@ Id,Label ...@@ -1461,7 +1461,7 @@ Id,Label
1459,0 1459,0
1460,0 1460,0
1461,1 1461,1
1462,1 1462,0
1463,1 1463,1
1464,0 1464,0
1465,0 1465,0
...@@ -1472,18 +1472,18 @@ Id,Label ...@@ -1472,18 +1472,18 @@ Id,Label
1470,1 1470,1
1471,1 1471,1
1472,1 1472,1
1473,1 1473,0
1474,0 1474,0
1475,1 1475,1
1476,1 1476,1
1477,1 1477,0
1478,1 1478,1
1479,1 1479,1
1480,0 1480,0
1481,1 1481,1
1482,0 1482,0
1483,0 1483,0
1484,0 1484,1
1485,0 1485,0
1486,0 1486,0
1487,1 1487,1
...@@ -1499,14 +1499,14 @@ Id,Label ...@@ -1499,14 +1499,14 @@ Id,Label
1497,0 1497,0
1498,0 1498,0
1499,1 1499,1
1500,0 1500,1
1501,0 1501,0
1502,0 1502,0
1503,0 1503,0
1504,0 1504,0
1505,0 1505,0
1506,0 1506,0
1507,1 1507,0
1508,1 1508,1
1509,0 1509,0
1510,1 1510,1
...@@ -1524,11 +1524,11 @@ Id,Label ...@@ -1524,11 +1524,11 @@ Id,Label
1522,1 1522,1
1523,0 1523,0
1524,0 1524,0
1525,1 1525,0
1526,0 1526,0
1527,0 1527,0
1528,0 1528,0
1529,0 1529,1
1530,0 1530,0
1531,0 1531,0
1532,0 1532,0
...@@ -1560,7 +1560,7 @@ Id,Label ...@@ -1560,7 +1560,7 @@ Id,Label
1558,0 1558,0
1559,0 1559,0
1560,1 1560,1
1561,1 1561,0
1562,1 1562,1
1563,0 1563,0
1564,1 1564,1
...@@ -1584,15 +1584,15 @@ Id,Label ...@@ -1584,15 +1584,15 @@ Id,Label
1582,1 1582,1
1583,1 1583,1
1584,0 1584,0
1585,1 1585,0
1586,0 1586,0
1587,0 1587,0
1588,1 1588,0
1589,1 1589,1
1590,1 1590,1
1591,1 1591,1
1592,0 1592,0
1593,1 1593,0
1594,0 1594,0
1595,1 1595,1
1596,1 1596,1
......
model2.py
View file @ e53add22
...@@ -7,14 +7,21 @@ from pprint import pprint as pp ...@@ -7,14 +7,21 @@ from pprint import pprint as pp
from math import log, exp from math import log, exp
import numpy as np import numpy as np
def doNormalize(X): removed=[]
def doNormalize(X,isTrain):
#do 0 mean 1 std normalization #do 0 mean 1 std normalization
x1 = np.array(X,dtype=float) x1 = np.array(X,dtype=float)
for i in range(len(X[0])): for i in range(len(X[0])):
col = x1[:,i] col = x1[:,i]
mean,std = col.mean(),col.std() mean,std = col.mean(),col.std()
std = std if std!=0.0 else 1.0 #std = std if std!=0.0 else 1.0
x1[:,i] = (x1[:,i]-mean)/std if i!=0:
if std<0.1 and isTrain:
removed.append(i)
else:
x1[:,i] = (x1[:,i]-mean)/max(std,1.0)
x1 = np.delete(x1, removed, axis=1)
return x1.tolist() return x1.tolist()
def getData(srcF, isTrain=True, addBias=True, normalize=True): def getData(srcF, isTrain=True, addBias=True, normalize=True):
...@@ -37,7 +44,7 @@ def getData(srcF, isTrain=True, addBias=True, normalize=True): ...@@ -37,7 +44,7 @@ def getData(srcF, isTrain=True, addBias=True, normalize=True):
y.append(entry) y.append(entry)
if normalize: if normalize:
X = doNormalize(X) X = doNormalize(X,isTrain)
#print(X[0]) #print(X[0])
return (np.array(X),np.array(y)) return (np.array(X),np.array(y))
...@@ -111,14 +118,21 @@ def fit(model, X, y, passes=1000): ...@@ -111,14 +118,21 @@ def fit(model, X, y, passes=1000):
dw1 += (model['lambda']/m)*w1 dw1 += (model['lambda']/m)*w1
dw2 += (model['lambda']/m)*w2 dw2 += (model['lambda']/m)*w2
w1 += -model['eta']*dw1 costs = []
w2 += -model['eta']*dw2 ws = []
for eta in model['eta']:
model['w1'] = w1 tw1 = w1-eta*dw1
model['w2'] = w2 tw2 = w2-eta*dw2
model['w1'] = tw1
model['w2'] = tw2
costs.append(cost(model,X,y))
ws.append((tw1,tw2))
idx = np.argmin(costs)
w1,w2 = ws[idx][0],ws[idx][1]
model['w1'],model['w2'] = w1,w2
if i % (passes/10)==0: if i % (passes/10)==0:
print(i,cost(model, X, y)) print(i,costs[idx])
return model return model
...@@ -126,15 +140,17 @@ def fit(model, X, y, passes=1000): ...@@ -126,15 +140,17 @@ def fit(model, X, y, passes=1000):
if __name__ == "__main__": if __name__ == "__main__":
np.random.seed(47) np.random.seed(47)
np.seterr(over='raise') np.seterr(over='raise')
X,y = getData("Train.csv")
tX,ty = getData("TestX.csv",isTrain=False)
model = {} model = {}
model = {'li':57,'lh':85,'lo':2,'lambda':0.1,'eta':0.01} model = {'li':X.shape[1]-1,'lh':int(3*(X.shape[1]-1)/2),'lo':2,'lambda':0.1,'eta':[0.01,0.06,0.1]}
# model['w1'] = np.random.randn(model['li']+1, model['lh'])/np.sqrt(model['li']+1) #58x28 # model['w1'] = np.random.randn(model['li']+1, model['lh'])/np.sqrt(model['li']+1) #58x28
# model['w2'] = np.random.randn(model['lh']+1, model['lo'])/np.sqrt(model['lh']+1) #29x2 # model['w2'] = np.random.randn(model['lh']+1, model['lo'])/np.sqrt(model['lh']+1) #29x2
model['w1'] = np.random.rand(model['li']+1, model['lh'])*0.24 - 0.12 model['w1'] = np.random.rand(model['li']+1, model['lh'])*0.24 - 0.12
model['w2'] = np.random.rand(model['lh']+1, model['lo'])*0.24 - 0.12 model['w2'] = np.random.rand(model['lh']+1, model['lo'])*0.24 - 0.12
X,y = getData("Train.csv")
tX,ty = getData("TestX.csv",isTrain=False)
#cost(model, X, y) #cost(model, X, y)
# for h in [57/3, 57/2, 2*57/3, 57, 3*57/2]: # for h in [57/3, 57/2, 2*57/3, 57, 3*57/2]:
# h=int(h) # h=int(h)
...@@ -142,7 +158,7 @@ if __name__ == "__main__": ...@@ -142,7 +158,7 @@ if __name__ == "__main__":
# model['w1'] = np.random.randn(model['li']+1, model['lh'])/np.sqrt(model['li']+1) #58x28 # model['w1'] = np.random.randn(model['li']+1, model['lh'])/np.sqrt(model['li']+1) #58x28
# model['w2'] = np.random.randn(model['lh']+1, model['lo'])/np.sqrt(model['lh']+1) #29x2 # model['w2'] = np.random.randn(model['lh']+1, model['lo'])/np.sqrt(model['lh']+1) #29x2
model = fit(model, X, y, passes=500) model = fit(model, X, y, passes=1500)
m = X.shape[0] m = X.shape[0]
py,y2=[],[] py,y2=[],[]
......
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