Created
March 28, 2017 19:13
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Calculate Product Moment Correlation Coefficient (Pearson Coefficient, r)
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import math | |
import random | |
class Buffer: | |
def __init__(self, one, two): | |
self.one = str(one) | |
self.two = str(two) | |
self.length = max((len(self.one), len(self.two))) + 1 | |
def __call__(self, obj): | |
return " " * (self.length - len(str(obj))) | |
class Bivariate: | |
def __init__(self, x, y): | |
self.x = x | |
self.y = y | |
self.data = [] | |
def add_point(self, x, y): | |
self.data.append((x, y)) | |
def pearson(self): | |
n = len(self.data) | |
sig_x = sum((d[0] for d in self.data)) | |
sig_x_sq = sum((d[0] ** 2 for d in self.data)) | |
sig_y = sum((d[1] for d in self.data)) | |
sig_y_sq = sum((d[1] ** 2 for d in self.data)) | |
sig_x_y = sum((d[0] * d[1] for d in self.data)) | |
Sxx = sig_x_sq - ((sig_x ** 2) / n) | |
Syy = sig_y_sq - ((sig_y ** 2) / n) | |
Sxy = sig_x_y - ((sig_x * sig_y) / n) | |
r = Sxy / math.sqrt(Sxx * Syy) | |
return r | |
def pretty_print(self): | |
row1 = self.x + Buffer(self.x, self.y)(self.x) | |
row2 = self.y + Buffer(self.x, self.y)(self.y) | |
for datum in self.data: | |
b = Buffer(datum[0], datum[1]) | |
row1 += str(datum[0]) + b(datum[0]) | |
row2 += str(datum[1]) + b(datum[1]) | |
print(row1) | |
print(row2) | |
## Answer Question | |
data = Bivariate("Rainfall", "Discharge") | |
data.add_point(1.3, 2.9) | |
data.add_point(1.9, 2.7) | |
data.add_point(4.2, 7.9) | |
data.add_point(3.7, 6.8) | |
data.add_point(1.8, 2.8) | |
data.add_point(2.5, 3.9) | |
data.add_point(2.6, 5.2) | |
data.add_point(0.5, 1.9) | |
data.pretty_print() | |
print("r = {}".format(data.pearson())) |
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