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https://github.com/c0de-archive/hacktoberfest-2018.git
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54 lines
1.2 KiB
Python
54 lines
1.2 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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def estimate_coef(x, y):
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# number of observations/points
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n = np.size(x)
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# mean of x and y vector
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m_x, m_y = np.mean(x), np.mean(y)
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# calculating cross-deviation and deviation about x
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SS_xy = np.sum(y*x - n*m_y*m_x)
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SS_xx = np.sum(x*x - n*m_x*m_x)
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# calculating regression coefficients
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b_1 = SS_xy / SS_xx
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b_0 = m_y - b_1*m_x
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return(b_0, b_1)
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def plot_regression_line(x, y, b):
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# plotting the actual points as scatter plot
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plt.scatter(x, y, color = "m",
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marker = "o", s = 30)
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# predicted response vector
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y_pred = b[0] + b[1]*x
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# plotting the regression line
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plt.plot(x, y_pred, color = "g")
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# putting labels
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plt.xlabel('x')
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plt.ylabel('y')
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# function to show plot
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plt.show()
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def main():
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# observations
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x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])
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# estimating coefficients
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b = estimate_coef(x, y)
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print("Estimated coefficients:\nb_0 = {} \\nb_1 = {}".format(b[0], b[1]))
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# plotting regression line
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plot_regression_line(x, y, b)
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if __name__ == "__main__":
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main()
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