69 lines
3.0 KiB
Python
69 lines
3.0 KiB
Python
# Karoo Multiclass Classifer Test
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# by Kai Staats, MSc UCT / AIMS
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# version 0.9.2.0
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'''
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This is a toy script, designed to allow you to play with multiclass classification using the same underlying function
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as employed by Karoo GP. Keep in mind that a linear multiclass classifier such as this is suited only for data which
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itself has a linear (eg: time series) component, else GP will struggle to force the data to fit.
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'''
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from numpy import arange
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while True:
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try:
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class_type = raw_input('\t Select (i)nfinite or (f)inite wing bins (default i): ')
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if class_type not in ('i','f',''): raise ValueError()
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class_type = class_type or 'i'; break
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except ValueError: print '\033[32mSelect from the options given. Try again ...\n\033[0;0m'
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n = range(1,100)
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while True:
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try:
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class_labels = raw_input('\t Enter the number of class labels (default 4): ')
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if class_labels not in str(n) and class_labels not in '': raise ValueError()
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if class_labels == '0': class_labels = 1; break
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class_labels = class_labels or 4; class_labels = int(class_labels); break
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except ValueError: print '\033[32m Enter a number from 3 including 100. Try again ...\n\033[0;0m'
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skew = (class_labels / 2) - 1
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min_val = 0 - skew - 1 # add a data point to the left
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if class_labels & 1: max_val = 0 + skew + 3 # add a data point to the right if odd number of class labels
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else: max_val = 0 + skew + 2 # add a data point to the right if even number of class labels
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print '\n\t class_labels =', range(class_labels)
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print '\t solutions = [', min_val, '...', max_val,']'
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print '\t skew =', skew, '\n'
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if class_type == 'i':
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for solution in arange(min_val, max_val, 0.5):
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for label in range(class_labels):
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if label == 0 and solution <= 0 - skew: # check for the first class
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fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas\033[1m', solution, '\033[0;0m\033[36m<=', 0 - skew, '\033[0;0m'
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elif label == class_labels - 1 and solution > label - 1 - skew: # check for the last class
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fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas\033[1m', solution, '\033[0;0m\033[36m>', label - 1 - skew, '\033[0;0m'
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elif label - 1 - skew < solution <= label - skew: # check for class bins between first and last
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fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas', label - 1 - skew, '<\033[1m', solution, '\033[0;0m\033[36m<=', label - skew, '\033[0;0m'
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else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
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# print ''
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if class_type == 'f':
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for solution in arange(min_val, max_val, .5):
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for label in range(class_labels):
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if label - 1 - skew < solution <= label - skew: # check for discrete, finite class bins
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fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas', label - 1 - skew, '<\033[1m', solution, '\033[0;0m\033[36m<=', label - skew, '\033[0;0m'
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else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
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# print ''
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