def regression(alpha, matrix, epsilon):
a=.1 #How do we determine a and b?
b=10
size= matrix.shape[1]-1 #don't want to include last column (y's)
theta= cf.randomThetas(a,b,size)
temp= np.copy(theta)
derVector=np.ones(size)
currentRowIndex= 0
grad= 199 #Just to ensure enters while loop
iterations=0
while iterations < 10000: # Need to update iteration value
iterations+=1
theta= np.copy(temp) #When you enter loop update theta to new values Note: this could also be at end of while loop just depends.
currentRow= np.array(matrix[currentRowIndex:currentRowIndex+1,:matrix.shape[1]-1]) #get row from matrix make into array
currentRow= currentRow[0] # Want to make 1d array currently 2d
rowDeviation = cf.deviation(currentRow, theta)
for i in range(size):
derVector[i]=cf.costDeriv(rowDeviation, theta[i])
temp[i]= theta[i] -(float(alpha)/size)*derVector[i]
if currentRowIndex==matrix.shape[0]-1:
courrentRowIndex=0
else:
currentRowIndex+=1
print costFunction(theta, currentRow, matrix, size)
return theta
def regression (alpha, epsilon, gamma, secondsToRun, data, ridge): if not ridge: gamma = 0 size = data[0].size - 1 thetas = Cost.randomThetas(0,0, size) ## make a random list of thetas cur = 0 timeout = time.time() + secondsToRun itera = 0 previousCost = 0 while True: va = np.asarray(data[cur]) v = va[0] thetaT = np.zeros(size) dev = Cost.deviation(v, thetas) cost = Cost.costFunction(thetas, data, gamma) if abs(cost - previousCost) < epsilon or timeout < time.time(): break for i in range(size): thetaT[i] = (1 - alpha * gamma) * thetas[i] - alpha * Cost.costDeriv(dev, v[i]) thetas = thetaT if cur < data.shape[0] - 1: # I don't think this is the number you want. You want the number of rows right? cur += 1 else: cur = 0 itera += 1 ##print costFunction(thetas, data[cur], ) ##print cur ##plt.plot(range(len(lst)), lst,'ro') ##plt.axis([0, len(lst), 0, 40]) ##plt.show() ##plot the size of gradient temp = cost previousCost = cost return thetas
def main(): a = np.ones(10) print cf.deviation(a, a) print cf.gradient(10, np.array([1,2,3]))