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# Use Genetic Programming for Classification and Symbolic Regression
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# by Kai Staats, MSc UCT / AIMS
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# ver. 20151018
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'''
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A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
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@ -11,7 +10,7 @@ this application. While your computer will not burst into flames nor will the su
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likely find more enjoyment of this particular flavour of GP with a little understanding of its intent and design.
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'''
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import sys; sys.path.append('modules/') # add the directory 'modules' to the current path
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import sys # sys.path.append('modules/') # add the directory 'modules' to the current path
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import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP()
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#++++++++++++++++++++++++++++++++++++++++++
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@ -2,9 +2,8 @@
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# Use Genetic Programming for Classification and Symbolic Regression
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# by Kai Staats, MSc UCT / AIMS
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# ver. 20151018
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import sys; sys.path.append('modules/') # add the directory 'modules' to the current path
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import sys # sys.path.append('modules/') # add the directory 'modules' to the current path
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import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP()
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# parameters configuration
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# Karoo Feature Set Prep
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# Prepare a balanced feature set
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# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
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# ver. 20151020
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import sys
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import numpy as np
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# Karoo GP Iris Plot
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# Plot a function generated by Karoo GP against a scatter of the Iris data
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# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
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# ver. 20151018
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# See https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ for a good plotting tutorial
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@ -9,9 +8,9 @@ import numpy as np
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import matplotlib.pyplot as mpl
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from mpl_toolkits.mplot3d import Axes3D
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# data = np.loadtxt('files/Iris_dataset/data_IRIS_setosa-vs-versicolor_3-col_PLOT.csv', delimiter=',', dtype = str)
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# data = np.loadtxt('files/Iris_dataset/data_IRIS_versicolor-vs-virginica_3-col_PLOT.csv', delimiter=',', dtype = str)
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data = np.loadtxt('files/Iris_dataset/data_IRIS_virginica-vs-setosa_3-col_PLOT.csv', delimiter=',', dtype = str)
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# data = np.loadtxt('../files/Iris_dataset/data_IRIS_setosa-vs-versicolor_3-col_PLOT.csv', delimiter=',', dtype = str)
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# data = np.loadtxt('../files/Iris_dataset/data_IRIS_versicolor-vs-virginica_3-col_PLOT.csv', delimiter=',', dtype = str)
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data = np.loadtxt('../files/Iris_dataset/data_IRIS_virginica-vs-setosa_3-col_PLOT.csv', delimiter=',', dtype = str)
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# http://stn.spotfire.com/spotfire_client_help/norm/norm_normalizing_columns.htm
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# to scale between 0 and 1: n - min(list) / (max(list) - min(list))
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# Karoo Multiclass Classifer Test
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# Play with quantity of class labels against a range of results
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# by Kai Staats, MSc UCT / AIMS
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# ver. 20151018
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from numpy import arange
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