Create CODE_OF_CONDUCT.md

- Rewrote certain numGen functions
- Removed ++ functions

.gitignore

@@ -1,2 +1,8 @@
 testdata/
 __pycache__/
+.idea/workspace.xml
+.idea/vcs.xml
+.idea/modules.xml
+.idea/misc.xml
+.idea/miner.iml
+.idea/libraries/R_User_Library.xml

CODE_OF_CONDUCT.md

@@ -0,0 +1,3 @@
+# Code of Conduct
+
+Be excellent to each other.

CONTRIBUTING.md

@@ -0,0 +1,13 @@
+# Contribution Guide
+
+1. Write your algorithm (and test it!)
+2. Choose a neat name for it (NAME in the following)
+3. Fork this repository
+4. Create subfolder src/algorithms/NAME with your algorithm and a readme.md-file
+5. Add your algorithm to the menu-script in the algorithm-folder
+6. Add your algorithm to the list above in the following format NAME | Author
+7. Create a pull request to merge the data (if your algorithm works)
+
+Thanks for your contribution!
+
+**Pay attention: Please don't commit test data. NEVER commit passwords or personal information!**

LICENSE

@@ -1,674 +1,21 @@
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-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
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-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
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-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
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-possible use to the public, the best way to achieve this is to make it
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-
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-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
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-    Copyright (C) <year>  <name of author>
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-    but WITHOUT ANY WARRANTY; without even the implied warranty of
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-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
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-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+MIT License
+
+Copyright (c) 2020 GB - Informatik
+
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+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,17 +1,17 @@
-# Collection Of Data Mining Algorithms (CODMA)
-
 [![License: AGPL v3](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
+# Collection Of Data Mining Algorithm
 
 ## What is this?
 
-This is the collection of our algorithm and its modules for data analyses
+This is a collection of some algorithms and modules we use for data analyses
 
 ## List of Algorithms
 
-| Name of Algorithm | Author |
-| ------------- | ------------- |
-| kmeansMkI.py  | [Tillmann](https://github.com/tchemn) and [Conrad](https://github.com/creyd) |
-| [ PENDING ]  | [ PENDING ]  |
+| Name of Algorithm | Name of File| Author |
+| ------------- | ------------- | ------------- |
+| k-Means  | kmeansMkI.py | [Tillmann](https://github.com/tchemn) and [Conrad](https://github.com/creyd) |
+| k-Means (2D)  | kmeansMkI_2d.py | [Tillmann](https://github.com/tchemn) and [Conrad](https://github.com/creyd) |
+| [ Your Algorithm ]  | [ Your File Name] | [ Your Name ]  |
 
 ## List of Modules
 
@@ -22,18 +22,6 @@ This is the collection of our algorithm and its modules for data analyses
 
 ## How to contribute
 
-1. Write your algorithm (or part of it)
-2. Choose a neat name for it (NAME in the following)
-3. Fork this repository (or with rights: add a branch `alg_NAME`)
-4. Create subfolder `src/algorithms/NAME`
-5. Add your algorithm to the `menu`-script in the `algorithm`-folder 
-6. Add your algorithm to the list above in the following format `NAME | Author`
-7. Create a `HowToUse`-file in your main folder
-**Only if your algorithm works:**
-8. Create a pull request to merge the data
-
-**Pay attention: NEVER commit test data, passwords or personal information!**
-
-Have fun... :)
+Take a look at [CONTRIBUTING.md](https://github.com/gb-i/data_mining_algorithms/blob/master/CONTRIBUTING.md) for guidelines.
 
 ~

src/algorithms/dmlib.py

@@ -1,40 +1,19 @@
-# Calculate the difference between two points giving the indexes of these data entries
+# Imports for dmlib
+import math
+
+
+# Calculate the difference between two points giving the indexes of these xdata entries
 def calcdiff(point1, point2):
     if int(point2) > int(point1):
         difference = int(point2) - int(point1)
     else:
         difference = int(point1) - int(point2)
-	# print("Datapoint: " + str(data[point1]) + " | Cluster: " + str(data[point2]) + " | Difference: " + str(difference))
-	return betrag(difference)
+    return difference
 
-# Get the absolute value of a number and returns it as int
-def betrag(number):
-	if number < 0:
-		number = int((-2 * number) / 2)
-	return number
 
-# Determine the highest int value in an array and returns is as an int
-def findHighest(data):
-	maximum = 0
-	for i in range(0, len(data)):
-		if int(data[i]) > maximum:
-			maximum = int(data[i])
-	return maximum
-
-def pp_calcdiff(data, clusterpoint):
-	max_diff = 0
-	new_cluster = 0
-	for item in range(0,len(data)):
-		if calcdiff(data[item], clusterpoint) > max_diff:
-			max_diff = calcdiff(data[item], clusterpoint)
-			new_cluster = data[item]
-	return new_cluster
-
-def pp_calcdiff_2(data, clusterpoint, clusterpoint_2):
-	max_diff = 0
-	new_cluster = 0
-	for item in range(0,len(data)):
-		if calcdiff(data[item], clusterpoint) + calcdiff(data[item], clusterpoint_2) > max_diff:
-			max_diff = calcdiff(data[item], clusterpoint)
-			new_cluster = data[item]
-	return new_cluster
+# Calculate the difference between two points in 2D space
+def calcdiff2d(point1, point2):
+    point1 = [int(i) for i in point1]
+    point2 = [int(i) for i in point2]
+    difference = math.sqrt(((point2[0]) - (point1[0])) ** 2 + ((point2[1]) - (point1[1])) ** 2)
+    return abs(difference)

src/algorithms/dmtest.py

@@ -1,34 +1,64 @@
 # For random generation of numbers import randint
 from random import randint, shuffle
 
-# Simple generator for test plzs (40-40-20 biased), returns 1D array of plzs
-def plzGen(entries):
+
+# Simple generator for test nums (40-40-20 biased), returns 1D array of nums
+def numGenLight(entries, shuffle, num_lenght):
     dataArray = []
-	plz_lenght = 5
     for i in range(0, int(entries)):
         if i < round(entries * 0.4):
-			plz = generateNumber(plz_lenght, 2)
+            num = generateNumber(num_lenght, 2)
         elif i >= round(entries * 0.4) and i < round(entries * 0.6):
-			plz = generateNumber(plz_lenght, 9)
+            num = generateNumber(num_lenght, 9)
         elif i >= round(entries * 0.6) and i < round(entries * 0.9):
-			plz = generateNumber(plz_lenght, 4)
+            num = generateNumber(num_lenght, 4)
         else:
-			plz = generateNumber(plz_lenght, randint(0,9))
-		dataArray.append(plz)
+            num = generateNumber(num_lenght, randint(0, 9))
+        dataArray.append(num)
+    if shuffle:
         shuffle(dataArray)
     return dataArray
 
+
 # Function for generating the content of one single row randomly
 def generateNumber(numberLenght, startingNumber):
     number = str(startingNumber)
     for length in range(0, numberLenght - 1):
-		number = number + str(randint(0,9))
+        number = number + str(randint(0, 9))
     return number
 
-# Function for writing data into a file (content = string, nameChunkStart and namePartStart are for better naming)
+
+# Function for writing data into a file
+# content = string, nameChunkStart and namePartStart are for better naming
 # /testdata/ folder has to be created at this point
 def writeFile(content, nameChunkStart, namePartStart):
     filenumber = int(nameChunkStart) + int(namePartStart)
     file = open("testdata/file" + str(filenumber) + ".txt", "w")
     for w in range(0, len(content)):
         file.write(content[w] + "\n")
+
+
+# Function for generating 'entries'x int_lenght'-long numbers in 'clusters' clusters
+def numGen(entries, cluster, int_lenght, suffle_value):
+    dataArray = []
+    clusterArray = []
+
+    for cluster_num in range(0, cluster):
+        clusterArray.append(randint(10, 99))
+
+    for item in range(0, entries):
+        decider = randint(0, 2)
+        if decider == 2:
+            dataArray.append(generateNumber(int_lenght, randint(1, 9)))
+        else:
+            cluster_decider = randint(0, cluster - 1)
+            dataArray.append(
+                generateNumber(
+                    int_lenght - 1,
+                    clusterArray[cluster_decider]
+                ))
+
+    if suffle_value:
+        shuffle(dataArray)
+
+    return dataArray

src/algorithms/k-means/kmeansMkI.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+# title:                kmeansMkI.py
+# description:        Our personal Python K-Means++ implementation
+# author:            Tillmann Brendel, Conrad Großer
+# license:            Pending
+# date:                26.05.2018
+# version:            1.3
+# usage:                python pyscript.py
+# notes:
+# dependencies:        matplotlib
+# known_issues:        When clusters are 'thin' or noice is to strong --> inaccurate
+# 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
+
+
+# CODE
+# Main function of the algorithm
+def kmeansmk1(data, clusters):
+    # Defining cluster points
+    for i in range(0, clusters):
+        globals()["cpoint_" + str(i)] = data[randint(0, len(data))]
+        print("Initial cluster " + str(i + 1) + ": " + str(globals()["cpoint_" + str(i)]))
+
+    # Get max value in the data array
+    highPoint = max(data)
+
+    # Define variables for running the algorithm (runs is just for benchmarking!)
+    done = False
+    runs = 0
+
+    # As long as calcClusters returns done it will rearange the clusters and assign the data to the clusters
+    while not done:
+        runs += 1
+        new_data = assignCluster(data, highPoint, clusters)
+        done = calcClusters(new_data, clusters)
+
+    # Printing final 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")
+
+    # Getting artificial array for visualizing 1D data in an 2D graphic of the size of the original data
+    anew = []
+    inew = 0
+    while inew < len(data):
+        anew.append(inew)
+        inew = inew + 1
+
+    # Drawing found clusters as lines
+    for i in range(0, clusters):
+        plt.axvline(x=int(globals()["cpoint_" + str(i)]), color='r')
+
+    # Showing graph
+    plt.scatter([int(x) for x in data], anew, marker='x', s=7, color='k')
+    plt.show()
+
+    return 0
+
+
+# Calculates middle values for each cluster, takes 2D array (item, assigned_cluster)
+def calcClusters(data, clusters):
+    changed = False
+    for cluster in range(0, clusters):
+        # Getting current cluster and saving it in temporary variable
+        prev_cluster = globals()["cpoint_" + str(cluster)]
+        # Sum of the cluster to calculate average difference between cluster center and data points
+        clustersum = 0
+        item_count = 0
+
+        for item in range(0, len(data[0])):
+            if data[1][item] == globals()["cpoint_" + str(cluster)]:
+                clustersum = clustersum + int(data[0][item])
+                item_count = item_count + 1
+        globals()["cpoint_" + str(cluster)] = round(clustersum / item_count)
+
+        # Checking if previous clusterpoint is equal to the one just calculated
+        if prev_cluster == globals()["cpoint_" + str(cluster)]:
+            changed = True
+
+    return changed
+
+
+def assignCluster(data, highPoint, clusters):
+    # Create a new data array for working
+    new_data = []
+    new_data.append(data)
+
+    # Create new array for assigned clusters of each value
+    data_assigned = []
+
+    # For each item in data find the minimal difference to a cluster and write it in the new data array in the second place (new_data[item][cluster_index])
+    for item in range(0, len(new_data[0])):
+        # Set the minimal cluster difference to the highest difference in the list to ease comparision
+        min_cluster = highPoint
+
+        # Check the difference between the point (item) and each cluster and set min_cluster to the smallest difference
+        for cluster in range(0, clusters):
+            if min_cluster > dmlib.calcdiff(data[item], globals()["cpoint_" + str(cluster)]):
+                min_cluster = dmlib.calcdiff(data[item], globals()["cpoint_" + str(cluster)])
+                assinged_cluster = globals()["cpoint_" + str(cluster)]
+        # Assign the minimal difference cluster to the data
+        data_assigned.append(assinged_cluster)
+    # Add the assigned values list to the new_data array
+    new_data.append(data_assigned)
+
+    return new_data
+
+
+# Startup function for collecting necesarry data
+def startup(data):
+    # 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 data? ") or in this case data
+
+    # For benchmarking starting the timer now
+    start_time = time.time()
+
+    # Firing up the engines!
+    kmeansmk1(data, clusters)
+
+    # Stopping benchmark
+    seconds = time.time() - start_time
+    print(str(seconds) + " seconds for execution")
+
+
+# Start the algorithm and generate test data
+data = dmtest.numGen(10000, 10, 5, True)
+startup(data)

src/algorithms/k-means/kmeansMkI_2d.py

@@ -0,0 +1,126 @@
+#!/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)

src/algorithms/kmeansMkI.py

@@ -1,140 +0,0 @@
-#!/usr/bin/env python
-#title:				kmeansMkI.py
-#description:		Our personal Python K-Means++ implementation
-#author:			Tillmann Brendel, Conrad Großer
-#license:			Pending
-#date:				26.05.2018
-#version:			1.2
-#usage:				python pyscript.py
-#notes:
-#dependencies:		mathplotlib
-#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 libaries for easy plotting
-import matplotlib.pyplot as plt
-
-# Importing own libaries Datamining Libary and Datamining Test
-import dmlib
-import dmtest
-
-# CODE
-# Main function of the algorithm
-def kmeansmk1(data, clusters):
-	# Defining cluster points
-	for i in range(0, clusters):
-		globals()["cpoint_" + str(i)] = data[randint(0, len(data))]
-		print("Initial cluster " + str(i + 1) + ": " + str(globals()["cpoint_" + str(i)]))
-
-	# Get max value in the data array
-	highPoint = dmlib.findHighest(data)
-
-	# Define variables for running the algorithm (runs is just for benchmarking!)
-	done = 0
-	runs = 0
-
-	# As long as calcClusters returns done it will rearange the clusters and assign the data to the clusters
-	while done == 0:
-		runs = runs + 1
-		new_data = assignCluster(data, highPoint, clusters)
-		done = calcClusters(new_data, clusters)
-
-	# Printing final 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")
-
-	# Getting artificial array for visualizing 1D data in an 2D graphic of the size of the original data
-	anew = []
-	inew = 0
-	while inew < len(data):
-		anew.append(inew)
-		inew = inew + 1
-
-	# Drawing found clusters as lines
-	for i in range(0, clusters):
-		plt.axvline(x=int(globals()["cpoint_" + str(i)]), color='r')
-
-	# Showing graph
-	plt.scatter([int(x) for x in data], anew, marker='x', s=7, color='k')
-	plt.show()
-
-	return 0
-
-# Calculates middle values for each cluster, takes 2D array (item, assigned_cluster)
-def calcClusters(data, clusters):
-	changed = 0
-	for cluster in range(0, clusters):
-		# Getting current cluster and saving it in temporary variable
-		prev_cluster = globals()["cpoint_" + str(cluster)]
-		# Sum of the cluster to calculate average difference between cluster center and data points 
-		clustersum = 0
-		item_count = 0
-
-		for item in range(0, len(data[0])):
-			if data[1][item] == globals()["cpoint_" + str(cluster)]:
-				clustersum = clustersum + int(data[0][item])
-				item_count = item_count + 1
-		globals()["cpoint_" + str(cluster)] = round(clustersum / item_count)
-
-		# Checking if previous clusterpoint is equal to the one just calculated
-		if prev_cluster == globals()["cpoint_" + str(cluster)]:
-			changed = 1
-
-	return changed
-
-def assignCluster(data, highPoint, clusters):
-	# Create a new data array for working
-	new_data = []
-	new_data.append(data)
-
-	# Create new array for assigned clusters of each value
-	data_assigned = []
-
-	# For each item in data find the minimal difference to a cluster and write it in the new data array in the second place (new_data[item][cluster_index])
-	for item in range(0, len(new_data[0])):
-		# Set the minimal cluster difference to the highest difference in the list to ease comparision
-		min_cluster = highPoint
-
-		# Check the difference between the point (item) and each cluster and set min_cluster to the smallest difference 
-		for cluster in range(0, clusters):
-			if min_cluster > dmlib.calcdiff(data[item], globals()["cpoint_" + str(cluster)]):
-				min_cluster = dmlib.calcdiff(data[item], globals()["cpoint_" + str(cluster)])
-				assinged_cluster = globals()["cpoint_" + str(cluster)]
-		# Assign the minimal difference cluster to the data
-		data_assigned.append(assinged_cluster)
-	# Add the assigned values list to the new_data array
-	new_data.append(data_assigned)
-
-	return new_data
-
-# Startup function for collecting necesarry data
-def startup(data):
-	# 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 data? ") or in this case data
-	
-	# For benchmarking starting the timer now
-	start_time = time.time()
-
-	# Firing up the engines!
-	kmeansmk1(data, clusters)
-
-	# Stopping benchmark
-	seconds = time.time() - start_time
-	print(str(seconds) + " seconds for execution")
-
-# Start the algorithm and generate test data
-data = dmtest.plzGen(10000)
-
-startup(data)

src/data_generators/randomi.py

@@ -11,11 +11,15 @@ from datetime import date
 import multiprocessing
 
 # randomI function which creates each file
+
+
 def randomI(units, rows, rowLength, partstart):
     for setcounter in range(0, units):
         writeFile(generateFile(rows, rowLength), setcounter, partstart)
 
 # Function for generating the content of one single file
+
+
 def generateFile(rows, rowLength):
     content = []
     for y in range(0, rows):
@@ -23,6 +27,8 @@ def generateFile(rows, rowLength):
     return content
 
 # Function for generating the content of one single row randomly
+
+
 def generateRow(rowLength):
     row = ""
     for z in range(0, rowLength):
@@ -30,12 +36,15 @@ def generateRow(rowLength):
     return row
 
 # Function for writing data into a file
+
+
 def writeFile(content, setcounter, partstart):
     filenumber = int(setcounter) + int(partstart)
     file = open("testdata/file" + str(filenumber) + ".txt", "w")
     for w in range(0, len(content)):
         file.write(content[w] + "\n")
 
+
 if __name__ == '__main__':
     # Getting the user input
     print("Hello World")

src/data_generators/randomi2.1.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-#title:				randomI2.1.py
-#description:		Personal 
-#author:			Tillmann Brendel, Conrad Großer
-#license:			Pending
-#date:				26.05.2018
-#version:			1.0
-#usage:				python pyscript.py
-#notes:
-#known_issues:
-#python_version:	3.x
-#==============================================================================
-
-# For random generation of numbers import randint
-from random import randint
-
-# Importing the time for benchmarking purposes
-import time
-from datetime import date
-
-# Importing for multi core processing
-import multiprocessing
-
-# randomI function which creates each file
-def randomI(units, rows, rowLength, partstart, cluster):
-	for setcounter in range(0, units):
-		writeFile(generateFile(rows, rowLength, cluster), setcounter, partstart)
-
-# Function for generating the content of one single file
-def generateFile(rows, rowLength, cluster):
-	content = []
-	for y in range(0, rows):
-		if y == 0:
-			if 1 == randint(1, cluster):
-				content.append(generate09())
-			else:
-				content.append(generatePLZ())
-		else:
-			content.append(generateRow(rowLength))
-	return content
-
-# Function for generating the content of one single row randomly
-def generateRow(rowLength):
-	row = ""
-	for z in range(0, rowLength):
-		row = row + str(randint(0, 9))
-	return row
-
-# Function for writing data into a file (content = string, setcount and partstart are for better naming)
-def writeFile(content, setcounter, partstart):
-	filenumber = int(setcounter) + int(partstart)
-	file = open("testdata/file" + str(filenumber) + ".txt", "w")
-	for w in range(0, len(content)):
-		file.write(content[w] + "\n")
-
-if __name__ == '__main__':
-	# Getting the user input
-	print("Hello World")
-	units = int(input("How many units would you like to generate? "))
-	rows = int(input("How many rows should each unit have? "))
-	rowLength = int(input("How long should each row be? "))
-	cores = int(input("How many cores do you want to use? "))
-	cluster = int(input("What fraction of postal codes should be in the 09xxx cluster? 1/"))
-
-    # Splitting up the units
-	count = int(0)
-	partsize = units / cores
-
-	# For benchmarking starting the timer now
-	start_time = time.time()
-
-	# Initialize and prepare cores for process
-	while count < cores:
-		partstart = partsize * count
-		globals()["p" + str(count)] = multiprocessing.Process(target=randomI, args=(int(partsize), rows, rowLength, partstart, cluster))
-		count = count + 1
-
-	# Starting each core
-	count = int(0)
-	while count < cores:
-		globals()["p" + str(count)].start()
-		print("Core " + str(count) + " started.")
-		count = count + 1
-
-	print("Working...")
-
-	# Joining each core for the process
-	count = int(0)
-	while count < cores:
-		globals()["p" + str(count)].join()
-		count = count + 1
-
-	# Finishing up the process
-	sec = time.time() - start_time
-	print("Data is generated. Have fun!")
-	print("randomI took " + str(sec) + " seconds for execution.")

src/main.py

@@ -1,24 +1,23 @@
 #!/usr/bin/env python
-#title:				main.py
-#description:		
-#author:			Conrad Großer
-#license:			https://github.com/tchemn/miner/blob/master/LICENSE
-#date:				02.06.2018
-#version:			0.1
-#usage:				PENDING
-#notes:				
-#dependencies:		
-#known_issues:		
-#python_version:	3.x
-#==============================================================================
+# title:				main.py
+# description:
+# author:			Conrad Großer
+# license:			https://github.com/tchemn/miner/blob/master/LICENSE
+# date:				02.06.2018
+# version:			0.1
+# usage:				PENDING
+# notes:
+# dependencies:
+# known_issues:
+# python_version:	3.x
+# ==============================================================================
 
 # IMPORTS
 
 # Importing the time for benchmarking purposes
 import time
-from datetime import date
 
-# CODE (FUNCTIONS)
+# CODE
 
 
 # EXECUTION

src/template.py

@@ -1,16 +1,16 @@
 #!/usr/bin/env python
-#title:				template.py
-#description:		Template for any programm 
-#author:			Authors seperated by comma
-#license:			License for the programm
-#date:				Date of creation
-#version:			Versionnumber
-#usage:				Description of how to use the programm quickly
-#notes:				Notes for parameters, thanks (...)
-#dependencies:		Preinstalled packages
-#known_issues:		Known issues in this version
-#python_version:	Compatible (tested) python version
-#==============================================================================
+# title:				template.py
+# description:		Template for any programm
+# author:			Authors seperated by comma
+# license:			License for the programm
+# date:				Date of creation
+# version:			Versionnumber
+# usage:				Description of how to use the programm quickly
+# notes:				Notes for parameters, thanks (...)
+# dependencies:		Preinstalled packages
+# known_issues:		Known issues in this version
+# python_version:	Compatible (tested) python version
+# ==============================================================================
 
 # IMPORTS