import pandas as pd
import xlsxwriter
from random import uniform
import math
import numpy as np


def calucateMean(df) -> tuple:
    mean = []
    maximum = []
    minimum = []
    for col in range(df.shape[1]):  # 0 ta 10
        mean.append(0)
        maximum.append(0)
        minimum.append(math.inf)
        for row in df:
            if (row[col] > maximum[col]):
                maximum[col] = row[col]
            if (row[col] < minimum[col]):
                minimum[col] = row[col]
            mean[col] += row[col]
        mean[col] = ((int)((mean[col] / len(df))*10000))/10000
    v = []
    for i in range(df.shape[1]):
        v.append(uniform(minimum[i], maximum[i]))
    vm = []
    for j in range(df.shape[1]):
        vm.append(mean[j] - v[j])
    return (mean, vm)


def matrixToxls(matrix, filename):
    workbook = xlsxwriter.Workbook(filename)
    worksheet = workbook.add_worksheet()
    row = 0
    for col, data in enumerate(matrix):
        worksheet.write_column(row, col, data)
    workbook.close()


def variance(col) -> list:
    return ((int)(sum([cell ** 2 for cell in matrixSubs[col]])*10000))/10000


def covariance(col1, col2):
    return ((int)(sum([p*q for p, q in zip(matrixSubs[col1], matrixSubs[col2])])*10000))/10000


def matrixCov():
    matrix = []
    for col1 in range(len(matrixSubs)):
        tmp = []
        for col2 in range(len(matrixSubs)):
            if (col1 == col2):
                tmp.append(variance(col1))
            else:
                tmp.append(covariance(col1, col2))
        matrix.append(tmp)
    return matrix


def corrMatrix(matrix):
    matrixCorr = []
    for col1 in range(len(matrix)):
        tmp = []
        for col2 in range(len(matrix)):
            if (col1 == col2):
                tmp.append(1)
            else:
                tmp.append((matrix[col1][col2]) /
                           ((matrix[col1][col1]*matrix[col2][col2])**0.5))
        matrixCorr.append(tmp)
    return matrixCorr


if __name__ == "__main__":
    df = pd.read_excel('dataset2.xls', sheet_name="forestfires").to_numpy()
    (mean, vm) = calucateMean(df)
    # print(mean)
    matrixSubs = []
    for col in range(df.shape[1]):
        tmp = []
        for row in df:
            tmp.append(row[col] - mean[col])
        matrixSubs.append(tmp)
    matrix = matrixCov()
    #
    CorrelationMatrix = corrMatrix(matrix)
    matrixToxls(CorrelationMatrix, "CorrelationMatrix.xls")