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] = mean[col] / len(df)
    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 variance(col, mean) -> list:
    var = 0
    for row in df:
        var += (row[col] - mean[col]) ** 2
    var /= (df.shape[0]-1)
    return var


def covariance(col1, col2, mean):
    cov = 0
    for row in df:
        cov += (row[col1] - mean[col1])*(row[col2] - mean[col2])
    cov /= (df.shape[0] - 1)
    return cov


def matrixCov(df, mean):
    matrix = []
    for col1 in range(df.shape[1]):
        tmp = []
        for col2 in range(df.shape[1]):
            if (col1 == col2):
                tmp.append(variance(col1, mean))
            else:
                tmp.append(covariance(col1, col2, mean))
        matrix.append(tmp)
    return matrix


def multiply(matrix1, matrix2):
    matrix = [0 for i in range(len(matrix1))]
    col = 0
    for i in range(len(matrix1)):
        for j in range(matrix2.shape[1]):
            matrix[i] = matrix1[i] * matrix2[i][col]
        col += 1
    return matrix


def multiplyTwoList(list1, list2):
    return sum([i*j for i, j in zip(list1, list2)])


if __name__ == "__main__":
    df = pd.read_excel('dataset2.xls', sheet_name="forestfires").to_numpy()
    (mean, vm) = calucateMean(df)
    matrix = matrixCov(df, mean)
    invc_matrix = np.linalg.inv(matrix)
    res = multiply(vm, invc_matrix)
    res = multiplyTwoList(res, vm)
    print(res**0.5)