<gh_stars>0


from __future__ import print_function
import sys
import numpy as np
from numpy import *
from scipy import stats


###################################################################################################


def isfloat(value):
    try:
        float(value)
        return True
    except:
        return False


def readData(fileName, lowFilter, highFilter):
    x = []
    y = []
    with open(fileName) as f:
        for i in f:
            a = i.split(",")
            if (len(a) == 17):
                if (isfloat(a[5]) and isfloat(a[11])):
                    if (float(a[5]) != 0 and float(a[11]) != 0):
                        if (float(a[11]) > lowFilter and float(a[11]) < highFilter):
                            x.append(float(a[5]))
                            y.append(float(a[11]))

    ax = np.array(x)
    ay = np.array(y)
    return np.vstack((ax, ay))


def gradient_descent_run(points, m_current, b_current, learningRate, num_iteration, precision):

    X = points[0]
    Y = points[1]
    previous_step_size = 1

    N = float(len(Y))
    iters = 0

    while (previous_step_size > precision and iters < num_iteration):
        y_current = (m_current * X) + b_current
        m_prev = m_current

        m_gradient = -(2 / N) * sum(X * (Y - y_current))
        b_gradient = -(2 / N) * sum(Y - y_current)
        new_learingRate = learningRate
       
        m_current = m_current - (new_learingRate * m_gradient)
        b_current = b_current - (new_learingRate * b_gradient)

        previous_step_size = abs(m_current - m_prev)
        iters = iters + 1
        if (iters % 100 == 0):
            print("Iteration: ", iters, " Beta0 :  ", "{0:.10f}".format(b_current), " Beta1 : ",
                  "{0:.10f}".format(m_current))

    return m_current, b_current


def run(fileName):
    points = readData(fileName, 5., 100.)
    # numerical Solution
    slope, intercept, r_value, p_value, std_err = stats.linregress(points[0], points[1])
    print("slope: ", slope)
    print("intercept: ", intercept)

    starting_b = 0
    starting_m = 0

    learningRate = 0.01
    num_iteration = 10000000
    precision = 0.00000001

    [m, b] = gradient_descent_run(points, starting_b, starting_m, learningRate, num_iteration, precision)

    print("======== Final Results ==============")
    print("Data after filter: ", points.shape)
    print("Beta0: ", b)
    print("Beta1: ", m)


if __name__ == "__main__":
    run(sys.argv[1])




from __future__ import print_function
import sys
import numpy as np
from numpy import *
from scipy import stats


###################################################################################################


def isfloat(value):
    try:
        float(value)
        return True
    except:
        return False


def readData(fileName, lowFilter, highFilter):
    x = []
    y = []
    with open(fileName) as f:
        for i in f:
            a = i.split(",")
            if (len(a) == 17):
                if (isfloat(a[5]) and isfloat(a[11])):
                    if (float(a[5]) != 0 and float(a[11]) != 0):
                        if (float(a[11]) > lowFilter and float(a[11]) < highFilter):
                            x.append(float(a[5]))
                            y.append(float(a[11]))

    ax = np.array(x)
    ay = np.array(y)
    return np.vstack((ax, ay))


def gradient_descent_run(points, m_current, b_current, learningRate, num_iteration, precision):

    X = points[0]
    Y = points[1]
    previous_step_size = 1

    N = float(len(Y))
    iters = 0

    while (previous_step_size > precision and iters < num_iteration):
        y_current = (m_current * X) + b_current
        m_prev = m_current

        m_gradient = -(2 / N) * sum(X * (Y - y_current))
        b_gradient = -(2 / N) * sum(Y - y_current)
        new_learingRate = learningRate
       
        m_current = m_current - (new_learingRate * m_gradient)
        b_current = b_current - (new_learingRate * b_gradient)

        previous_step_size = abs(m_current - m_prev)
        iters = iters + 1
        if (iters % 100 == 0):
            print("Iteration: ", iters, " Beta0 :  ", "{0:.10f}".format(b_current), " Beta1 : ",
                  "{0:.10f}".format(m_current))

    return m_current, b_current


def run(fileName):
    points = readData(fileName, 5., 100.)
    # numerical Solution
    slope, intercept, r_value, p_value, std_err = stats.linregress(points[0], points[1])
    print("slope: ", slope)
    print("intercept: ", intercept)

    starting_b = 0
    starting_m = 0

    learningRate = 0.01
    num_iteration = 10000000
    precision = 0.00000001

    [m, b] = gradient_descent_run(points, starting_b, starting_m, learningRate, num_iteration, precision)

    print("======== Final Results ==============")
    print("Data after filter: ", points.shape)
    print("Beta0: ", b)
    print("Beta1: ", m)


if __name__ == "__main__":
    run(sys.argv[1])
################################################################################################### # numerical Solution