model = linear_model.logReg(maxEvals=400)
model.fit(XBin, yBin)
print("\nlogReg Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logReg Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.1":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL2(lammy=1.0, maxEvals=400)
model.fit(XBin, yBin)
print("\nlogRegL2 Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logRegL2 Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.2":
data = utils.load_dataset("logisticData")
print(type(data))
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400)
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logReg(maxEvals=400, verbose=1)
model.fit(XBin,yBin)
print("\nlogReg Training error %.3f" % utils.classification_error(model.predict(XBin), yBin))
print("logReg Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.1":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
# Fit logRegL2 model
model = linear_model.logRegL2(lammy=1.0, maxEvals=400, verbose=1)
model.fit(XBin,yBin)
print("\nlogRegL2 Training error %.3f" % utils.classification_error(model.predict(XBin), yBin))
print("logRegL2 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.2":
# Load Binary and Multi -class data
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
# Fit logRegL1 model
model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400, verbose=1)
model.fit(XBin,yBin)
model = linear_model.logReg(maxEvals=400)
model.fit(XBin, yBin)
print("\nlogReg Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logReg Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.1":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL2(maxEvals=400, l=1.0)
model.fit(XBin, yBin)
print("\nlogRegL2 Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logRegL2 Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.2":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL1(l=1.0, maxEvals=400)
model.fit(XBin, yBin)
