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import MPCLinearRegression
import matplotlib.pyplot as plt
import numpy as np
import csv

m = MPCLinearRegression.MPCLinearRegression("localhost:1235", "141.20.33.236:1234")


    

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m.fit(csv_file="../../data/VA_Testdaten_Körpergröße.csv", owned_columns="4")


    

    




localhost:1235 waiting for peer 141.20.33.236:1234

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koerper = range(140, 240, 10)
gender = ["m", "w"]

colors = ['b', 'r']

csv_file='../../data/VA_Testdaten_Gesamt.csv'
columns = [4,3]
data = [[],[]]

with open(csv_file, "r") as f:
    reader = csv.reader(f, delimiter=";")
    for i, row in enumerate(reader):
        is_man = False
        for x in row:
            if x == 'm':
                is_man = True
        if i != 0:
            newrow = []
            for col in columns:
                cell = row[col]  
                newrow.append(float(cell))
            (data[0] if is_man else data[1]).append(newrow)

# transpose matrix
data[0] = [list(x) for x in zip(*data[0])]
data[1] = [list(x) for x in zip(*data[1])]

for i,g in enumerate(gender):
    schuh = [m.predict({"Geschlecht": g, "Koerpergroesse": k}) for k in koerper]
    plt.plot(koerper, schuh, colors[i])
    plt.scatter(data[i][0], data[i][1], c=colors[i])
plt.show()


    

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