##########
#For linear
import linear
h = linear.LinearClassifier({'lossFunction': linear.SquaredLoss(), 'lambda': 0, 'numIter': 100, 'stepSize': 0.5})
runClassifier.trainTestSet(h, datasets.TwoDAxisAligned)
X = datasets.TwoDAxisAligned.X
Y = datasets.TwoDAxisAligned.Y
#mlGraphics.plotLinearClassifier(h, X, Y)
h = linear.LinearClassifier({'lossFunction': linear.SquaredLoss(), 'lambda': 10, 'numIter': 100, 'stepSize': 0.5})
runClassifier.trainTestSet(h, datasets.TwoDAxisAligned)
h = linear.LinearClassifier({'lossFunction': linear.SquaredLoss(), 'lambda': 10, 'numIter': 100, 'stepSize': 0.5})
runClassifier.trainTestSet(h, datasets.TwoDDiagonal)
h = linear.LinearClassifier({'lossFunction': linear.HingeLoss(), 'lambda': 1, 'numIter': 100, 'stepSize': 0.5})
runClassifier.trainTestSet(h, datasets.TwoDDiagonal)
log = linear.LinearClassifier({'lossFunction': linear.LogisticLoss(), 'lambda': 1, 'numIter': 100, 'stepSize': 0.5})
runClassifier.trainTestSet(log, datasets.TwoDDiagonal)
# # Training accuracy 0.98, test accuracy 0.86
# print(f)
# # w=array([ 1.17110065, 4.67288657])
#
# # LogisticLoss
#
# f = linear.LinearClassifier({'lossFunction': linear.LogisticLoss(), 'lambda': 10, 'numIter': 100, 'stepSize': 0.5})
# runClassifier.trainTestSet(f, datasets.TwoDDiagonal)
# # Training accuracy 0.99, test accuracy 0.86
# print(f)
# # w=array([ 0.29809083, 1.01287561])
# WU5
print("Logistic:")
f = linear.LinearClassifier({
'lossFunction': linear.LogisticLoss(),
'lambda': 1,
'numIter': 100,
'stepSize': 0.5
})
runClassifier.trainTestSet(f, datasets.WineDataBinary)
large = [
0.606423261902, 0.689199007903, 0.710890552154, 0.770124769156,
0.883289753118
]
small = [
-1.1695212164, -0.765309390643, -0.683593167789, -0.629590728143,
-0.532191672468
]