#!/usr/bin/env python
# title:                kmeansMkI.py
# description:      Our personal Python K-Means++ implementation
# author:           Tillmann Brendel, Conrad Großer
# license:          Pending
# date:             04.06.2018
# version:          1.6
# usage:                python pyscript.py
# notes:
# known_issues:
# python_version:   3.x
# ==============================================================================

# IMPORTS

# Importing the time for benchmarking purposes
import time

# For random generation of numbers import randint
from random import randint

# Importing libaries for easy plotting
import matplotlib.pyplot as plt

# Importing own libaries Datamining Libary and Datamining Test
import dmlib
import dmtest


# Main function of the algorithm
def kmeansmk1(xdata, ydata, clusters):
    # Defining cluster points
    for i in range(0, clusters):
        globals()["cpoint_" + str(i)] = [xdata[randint(0, len(xdata))], ydata[randint(0, len(ydata))]]
        print("Initial cluster " + str(i + 1) + ": " + str(globals()["cpoint_" + str(i)]))

    # Get the maximum of the data
    highpointx = max(xdata)
    highpointy = max(ydata)

    # Define variables for running the algorithm (runs is just as important as every other variable)
    done = False
    runs = 0

    # As long as calcClusters returns False it will rearrange the clusters and assign the data to the clusters
    while not done:
        runs += 1
        assigned_points = assignCluster(xdata, ydata, clusters, highpointx, highpointy)
        # assigned_points consists of the clusternumbers
        done = calcClusters(xdata, ydata, assigned_points, clusters)

    for i in range(0, clusters):
        print("Endcluster " + str(i + 1) + " is calculated to be at  " + str(globals()["cpoint_" + str(i)]) + " after " + str(runs) + " runs")
    for i in range(0, clusters):
        plt.plot(globals()["cpoint_" + str(i)][0], globals()["cpoint_" + str(i)][1], 'ro')

    plt.scatter([int(x) for x in xdata], [int(y) for y in ydata], marker='x', s=7, color='k')
    plt.show()


# Calculates middle values for each cluster, takes 2D array (item, assigned_cluster)
def calcClusters(xdata, ydata, assigned_points, clusters):
    for cluster in range(0, clusters):
        cpointunchanged = True
        globals()["oldcpoint_" + str(cluster)] = globals()["cpoint_" + str(cluster)]
        clustersumx = 0
        clustersumy = 0
        count = 0

        for item in range(0, len(xdata)):
            if assigned_points[item] == cluster:
                clustersumx = clustersumx + int(xdata[item])
                clustersumy = clustersumy + int(ydata[item])
                count = count + 1

        globals()["cpoint_" + str(cluster)] = [round(clustersumx / count), round(clustersumy / count)]

        # checking if old clusterpoint is equal to the one just calculated
        if globals()["oldcpoint_" + str(cluster)] != globals()["cpoint_" + str(cluster)]:
            cpointunchanged = False

    return cpointunchanged


def assignCluster(xdata, ydata, clusters, highpointx, highpointy):
    data_assigned = []
    assigned_cluster = 0
    resetdist = dmlib.calcdiff2d([0, 0], [highpointx, highpointy])

    for item in range(0, len(xdata)):
        olddistance = resetdist
        for cluster in range(0, clusters):
            distance = dmlib.calcdiff2d(globals()["cpoint_" + str(cluster)], [xdata[item], ydata[item]])

            if distance < olddistance:
                olddistance = distance
                assigned_cluster = cluster

        data_assigned.append(assigned_cluster)

    return data_assigned


# Startup function for collecting necesarry xdata
def startup(xdata, ydata):
    # Using two clusters for testing
    clusters = int(input("How many clusters are known? "))
    # cores = input("How many cores should be used? ")
    # path = input("Where is the xdata? ") or in this case xdata

    # For benchmarking starting the timer now
    start_time = time.time()

    # Firing up the engines!
    kmeansmk1(xdata, ydata, clusters)

    # Stopping benchmark
    seconds = time.time() - start_time
    print(str(seconds) + " seconds for execution")


# Start the algorithm and generate test xdata
xdata = dmtest.numGenLight(10000, False, 5)
ydata = dmtest.numGenLight(10000, False, 2)

startup(xdata, ydata)