Gradient Descent in Two Dimensions in Python

Question

I'm having trouble understanding gradient descent in two dimensions. Say I have function f(x,y)=x**2-xy where df/dx = 2x-y and df/dy = -x.

So for point df(2,3), the output vector is [1, -2].T. Wherever vector [1,-2] is pointing is in the direction of steepest ascent (aka output of f(x,y)). I should choose a fixed step size, and find the direction that such a step of that size increase f(x,y) the most. If I want to descend, I want to find the direction that increase -f(x,y) most quickly?

If my intuition is right, how would you code this? Say I'm starting at point (x=0, y=5)and I want to perform a gradient descent to find the minimum value.

step_size = 0.01
precision = 0.00001 #stopping point
enter code here??

score 0 · Accepted Answer · answered Sep 24 '16 at 23:48

Here is the implementation of Gradient descent with matplotlib visualization:

import csv
import math
def loadCsv(filename):
    lines = csv.reader(open(filename, "r"))
    dataset = list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
    return dataset

def h(o1,o2,x):
    ans=o1+o2*x
    return ans

def costf(massiv,p1,p2):
    sum1=0.0
    sum2=0.0
    for x,y in massiv:
        sum1+=(math.pow(h(o1,o2,x)-y,2))
    sum2=(1.0/(2*len(massiv)))*sum1
    return sum1,sum2

def gradient(massiv,er,alpha,o1,o2,max_loop=1000):
    i=0
    J,e=costf(massiv,o1,o2)
    conv=False
    m=len(massiv)
    while conv!=True:
        sum1=0.0
        sum2=0.0
        for x,y in massiv:
            sum1+=(o1+o2*x-y)
            sum2+=(o1+o2*x-y)*x
        grad0=1.0/m*sum1
        grad1=1.0/m*sum2

        temp0=o1-alpha*grad0
        temp1=o2-alpha*grad1
        print(temp0,temp1)
        o1=temp0
        o2=temp1
        e=0.0
        for x,y in massiv:
            e+=(math.pow(h(o1,o2,x)-y,2))
        if abs(J-e)<=ep:
            print('Successful\n')
            conv=True

        J=e

        i+=1
        if i>=max_loop:
            print('Too much\n')
            break
    return o1,o2


#data = massiv
data=loadCsv('ex1data1.txt')
o1=0.0 #temp0=0
o2=1.0 #temp1=1
alpha=0.01
ep=0.01
t0,t1=gradient(data,ep,alpha,o1,o2)
print('temp0='+str(t0)+' \ntemp1='+str(t1))

x=35000
while x<=70000:
    y=h(t0,t1,x)
    print('x='+str(x)+'\ny='+str(y)+'\n')
    x+=5000

maxx=data[0][0]
for q,w in data:
    maxx=max(maxx,q)
maxx=round(maxx)+1
line=[]
ll=0
while ll<maxx:
    line.append(h(t0,t1,ll))
    ll+=1
x=[]
y=[]
for q,w in data:
    x.append(q)
    y.append(w)

import matplotlib.pyplot as plt
plt.plot(x,y,'ro',line)
plt.ylabel('some numbers')
plt.show()

Matplotlib output:

ex1data1.txt can be dowloaded from here: ex1data1.txt

The code can be executed as-is in Anaconda distribution with Python 3.5.

Gradient Descent in Two Dimensions in Python

1 Answers1