ch08_gradient_descent.py

# -*- coding: utf-8 -*-


from __future__ import division, unicode_literals
from collections import Counter
import math, random

from ch04_linear_algebra import distance, vector_subtract, scalar_multiply


# 8.1. 경사 하강법에 숨은 의미

def sum_of_squares(v):
    """ Computes the sum of squared elements in v """
    return sum(v_i ** 2 for v_i in v)


# 8.2. 경사 추정하기

def difference_quotient(f, x, h):
    return (f(x + h) - f(x)) / h


def plot_estimated_derivative():
    """ 그림 8-3. 나름 유용한 미분 근사값 """

    def square(x):
        return x * x

    def derivative(x):
        return 2 * x

    derivative_estimate = lambda x: difference_quotient(square, x, h=0.00001)

    # 두 계산식에 따른 결과값이 거의 비슷함을 보여주기 위한 그래프
    import matplotlib.pyplot as plt
    x = range(-10, 10)
    plt.title("실제 미분값 vs. 근사값")
    plt.plot(x, map(derivative, x), 'rx', label='실제값')           # 빨간색 x
    plt.plot(x, map(derivative_estimate, x), 'b+', label='근사값')  # 파란색 +
    plt.legend(loc=9)                                               # 상단 중앙 범례
    plt.show()                                                      # 보라색 *가 나와주길!


def partial_difference_quotient(f, v, i, h):
    """ 함수 f의 i번째 편도함수가 v에서 가지는 값 """

    w = [v_j + (h if j == i else 0)     # h를 v의 i번째 변수에만 더해주자
         for j, v_j in enumerate(v)]

    return (f(w) - f(v)) / h


def estimate_gradient(f, v, h=0.00001):
    return [partial_difference_quotient(f, v, i, h)
            for i, _ in enumerate(v)]


# 8.3. 경사 적용하기

def step(v, direction, step_size):
    """ move step_size in the direction from v"""

    return [v_i + step_size * direction_i
            for v_i, direction_i in zip(v, direction)]


def sum_of_squares_gradient(v):
    return [2 * v_i for v_i in v]


# 8.4. 적절한 스텝 크기 정하기

def safe(f):
    """define a new function that wraps f and return it"""
    def safe_f(*args, **kwargs):
        try:
            return f(*args, **kwargs)
        except:
            return float('inf')         # this means "infinity" in Python
    return safe_f


# 8.5. 전부 조합하기

def minimize_batch(target_fn, gradient_fn, theta_0, tolerance=0.000001):
    """use gradient descent to find theta that minimizes target function"""

    step_sizes = [100, 10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001]

    theta = theta_0                           # set theta to initial value
    target_fn = safe(target_fn)               # safe version of target_fn
    value = target_fn(theta)                  # value we're minimizing

    while True:
        gradient = gradient_fn(theta)
        next_thetas = [step(theta, gradient, -step_size)
                       for step_size in step_sizes]

        # choose the one that minimizes the error function
        next_theta = min(next_thetas, key=target_fn)
        next_value = target_fn(next_theta)

        # stop if we're "converging"
        if abs(value - next_value) < tolerance:
            return theta
        else:
            theta, value = next_theta, next_value


def negate(f):
    """return a function that for any input x returns -f(x)"""
    return lambda *args, **kwargs: -f(*args, **kwargs)


def negate_all(f):
    """the same when f returns a list of numbers"""
    return lambda *args, **kwargs: [-y for y in f(*args, **kwargs)]


def maximize_batch(target_fn, gradient_fn, theta_0, tolerance=0.000001):
    return minimize_batch(negate(target_fn),
                          negate_all(gradient_fn),
                          theta_0,
                          tolerance)


# 8.6. SGD

def in_random_order(data):
    """generator that returns the elements of data in random order"""
    indexes = [i for i, _ in enumerate(data)]  # create a list of indexes
    random.shuffle(indexes)                    # shuffle them
    for i in indexes:                          # return the data in that order
        yield data[i]


def minimize_stochastic(target_fn, gradient_fn, x, y, theta_0, alpha_0=0.01):

    data = zip(x, y)
    theta = theta_0                             # initial guess
    alpha = alpha_0                             # initial step size
    min_theta, min_value = None, float("inf")   # the minimum so far
    iterations_with_no_improvement = 0

    # if we ever go 100 iterations with no improvement, stop
    while iterations_with_no_improvement < 100:
        value = sum( target_fn(x_i, y_i, theta) for x_i, y_i in data )

        if value < min_value:
            # if we've found a new minimum, remember it
            # and go back to the original step size
            min_theta, min_value = theta, value
            iterations_with_no_improvement = 0
            alpha = alpha_0
        else:
            # otherwise we're not improving, so try shrinking the step size
            iterations_with_no_improvement += 1
            alpha *= 0.9

        # and take a gradient step for each of the data points
        for x_i, y_i in in_random_order(data):
            gradient_i = gradient_fn(x_i, y_i, theta)
            theta = vector_subtract(theta, scalar_multiply(alpha, gradient_i))

    return min_theta


def maximize_stochastic(target_fn, gradient_fn, x, y, theta_0, alpha_0=0.01):
    return minimize_stochastic(negate(target_fn),
                               negate_all(gradient_fn),
                               x, y, theta_0, alpha_0)


if __name__ == "__main__":
    print "using the gradient"

    v = [random.randint(-10,10) for i in range(3)]
    tolerance = 0.0000001

    while True:
        #print v, sum_of_squares(v)
        gradient = sum_of_squares_gradient(v)   # compute the gradient at v
        next_v = step(v, gradient, -0.01)       # take a negative gradient step
        if distance(next_v, v) < tolerance:     # stop if we're converging
            break
        v = next_v                              # continue if we're not

    print "minimum v", v
    print "minimum value", sum_of_squares(v)
    print

    print "using minimize_batch"

    v = [random.randint(-10,10) for i in range(3)]

    v = minimize_batch(sum_of_squares, sum_of_squares_gradient, v)

    print "minimum v", v
    print "minimum value", sum_of_squares(v)