Misc Cleanup Update

src/algorithms/k-means/kmeansMkI_2d.py

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+#!/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
+from datetime import date
+
+# For random generation of numbers import randint
+from random import randint
+
+# Importing libary for multi core processing
+import multiprocessing
+
+# Importing libaries for easy plotting
+import numpy as np
+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)