'''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)