'''Runs training until cost values converge to within some interval'''
val = self.linreg(learning_rate, ind, dep)
old_val = 0
#Can change this variable to decide how much convergence is wanted
while np.absolute(val-old_val) > 1:
old_val = val
val = self.linreg(learning_rate, ind, dep)
self.getTheta()
def getTheta(self):
'''Prints out Value for current weight and bias variables'''
print "Weight Bias"
print self.weight, self.bias
if __name__ == '__main__':
#command line to run this properly
#python NiceLinReg.py data.csv [2,3] 1
np.random.seed(42)
loader = ReadData()
loader.load(sys.argv[1], sys.argv[2], int(sys.argv[3]))
print "Temp Only"
tempOnly = NiceLinReg()
dailyTemp = loader.getInd(0)
DOJIA = loader.getDep()
tempOnly.train(.000005, dailyTemp, DOJIA)
print "\nDiff in Temp and avg highest recorded temp"
diff = NiceLinReg()
diffList = loader.diff(0,1)
diff.train(0.000000000049, diffList, DOJIA)
