spacecruft
/
karoo_gp
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

version number update only

pull/4/head
Kai Staats 2016-09-19 14:37:35 -06:00
parent e219a6b9c5
commit 8130906469
4 changed files with 21 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
tools/karoo_iris_plot.py
View File

@ -1,6 +1,6 @@
# Karoo Iris Plot
# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
# version 0.9.2.0
# version 0.9.2.1
import sys
import numpy as np

36
tools/karoo_multiclassifier.py
View File

@ -1,6 +1,6 @@
# Karoo Multiclass Classifer Test
# by Kai Staats, MSc UCT / AIMS
# version 0.9.2.0
# version 0.9.2.1
'''
This is a toy script, designed to allow you to play with multiclass classification using the same underlying function
@ -20,7 +20,7 @@ while True:
n = range(1,100)
while True:
try:
class_labels = raw_input('\t Enter the number of class labels (default 4): ')
class_labels = raw_input('\t Enter the number of class labels / solutions (default 4): ')
if class_labels not in str(n) and class_labels not in '': raise ValueError()
if class_labels == '0': class_labels = 1; break
class_labels = class_labels or 4; class_labels = int(class_labels); break
@ -32,36 +32,36 @@ min_val = 0 - skew - 1 # add a data point to the left
if class_labels & 1: max_val = 0 + skew + 3 # add a data point to the right if odd number of class labels
else: max_val = 0 + skew + 2 # add a data point to the right if even number of class labels
print '\n\t class_labels =', range(class_labels)
print '\t solutions = [', min_val, '...', max_val,']'
print '\n\t solutions =', range(class_labels)
print '\t results = [', min_val, '...', max_val,']'
print '\t skew =', skew, '\n'
if class_type == 'i':
for solution in arange(min_val, max_val, 0.5):
for label in range(class_labels):
for result in arange(min_val, max_val, 0.5):
for solution in range(class_labels):
if label == 0 and solution <= 0 - skew: # check for the first class
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'
if solution == 0 and result <= 0 - skew: # check for the first class
fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas\033[1m', result, '\033[0;0m\033[36m<=', 0 - skew, '\033[0;0m'
elif label == class_labels - 1 and solution > label - 1 - skew: # check for the last class
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'
elif solution == class_labels - 1 and result > solution - 1 - skew: # check for the last class
fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas\033[1m', result, '\033[0;0m\033[36m>', solution - 1 - skew, '\033[0;0m'
elif label - 1 - skew < solution <= label - skew: # check for class bins between first and last
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'
elif solution - 1 - skew < result <= solution - skew: # check for class bins between first and last
fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas', solution - 1 - skew, '<\033[1m', result, '\033[0;0m\033[36m<=', solution - skew, '\033[0;0m'
else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
else: fitness = 0 #; print '\t\033[36m no match for', result, 'in class', solution, '\033[0;0m' # no class match
# print ''
if class_type == 'f':
for solution in arange(min_val, max_val, .5):
for label in range(class_labels):
for result in arange(min_val, max_val, .5):
for solution in range(class_labels):
if label - 1 - skew < solution <= label - skew: # check for discrete, finite class bins
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'
if solution - 1 - skew < result <= solution - skew: # check for discrete, finite class bins
fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas', solution - 1 - skew, '<\033[1m', result, '\033[0;0m\033[36m<=', solution - skew, '\033[0;0m'
else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
else: fitness = 0 #; print '\t\033[36m no match for', result, 'in class', solution, '\033[0;0m' # no class match
# print ''

2
tools/karoo_normalise.py
View File

@ -1,6 +1,6 @@
# Karoo Data Normalisation
# by Kai Staats, MSc UCT
# version 0.9.2.0
# version 0.9.2.1
import sys
import numpy as np

2
tools/karoo_sort.py
View File

@ -1,6 +1,6 @@
# Karoo Dataset Builder
# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
# version 0.9.2.0
# version 0.9.2.1
import sys
import numpy as np