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Ballgorithm.py
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Ballgorithm.py
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import numpy as np
from sklearn.datasets import load_svmlight_file
from sklearn.datasets import dump_svmlight_file
from sklearn.cluster import KMeans
import random
import math
def main():
n_classes = 3
orig_dim = 100
radius = 4
ball_capacity = np.matrix(20)
max_req_balls = 0
n_balls = 0
balls_limit = 1000
threshold_ratio = 0.1
hit_count = 0
ball_classes = []
ball_capacity_classes = []
ball_centres = np.matrix([])
a = [100]*orig_dim
b = [1]*orig_dim
for i in range(n_classes):
ball_classes.append(np.matrix([a]))
ball_capacity_classes.append(np.matrix(0))
#print(ball_capacity_classes[0])
#print(ball_capacity_classes[1])
#print(ball_classes[1])
# #print(ball_classes[2])
tr_data = load_svmlight_file("train60000.txt")
X = np.matrix(tr_data[0].toarray()); # Converts sparse matrices to dense
Y = np.matrix(tr_data[1]);
Y = Y.T
newX = np.matrix(np.zeros(X.shape[1]))
newY = np.zeros(1)
classes = np.matrix([])
for j in range(X.shape[0]):
##print(j)
x = X[j]
y = Y[j]
##print((ball_classes[int(y)-1]).)
if((ball_classes[int(y)-1]).shape[0]==1):
ball_classes[int(y)-1] = np.append(ball_classes[int(y)-1],x,axis = 0)
ball_capacity_classes[int(y)-1] = np.append(ball_capacity_classes[int(y)-1],ball_capacity,axis = 0)
n_balls = n_balls + 1
else:
dists = ball_classes[int(y)-1] - x
dists = np.square(dists)
dists = np.sum(dists,axis = 1)
##print(min(dists))
if(min(dists) < radius**2 and ball_capacity_classes[int(y)-1][np.argmin(dists)] > 0):
##print(np.argmin(dists))
hit_count = hit_count+1
ball_capacity_classes[int(y)-1][np.argmin(dists)] = ball_capacity_classes[int(y)-1][np.argmin(dists)]-1
# if(ball_capacity_classes[int(y)-1][np.argmin(dists)] == 0):
# n_balls = n_balls-1
##print(hit_count)
else:
ball_classes[int(y)-1] = np.append(ball_classes[int(y)-1],x,axis = 0)
ball_capacity_classes[int(y)-1] = np.append(ball_capacity_classes[int(y)-1],ball_capacity,axis = 0)
n_balls = n_balls+1
# print(n_balls, balls_limit)
if(n_balls == balls_limit):
print("CAPACITY FULL... DUMPING")
for i in range(n_classes):
ball_classes[i] = ball_classes[i][1:]
#print(i)
if(classes.size == 0):
balls_pack = ball_classes[0]
classes = np.repeat(1,ball_classes[0].shape[0],axis = 0)
else :
classes = np.append(classes,np.repeat(1,ball_classes[0].shape[0],axis = 0))
balls_pack = np.append(balls_pack,ball_classes[0],axis = 0)
for i in range(1,n_classes):
balls_pack = np.append(balls_pack,ball_classes[i],axis = 0)
classes = np.append(classes,np.repeat(i+1,ball_classes[i].shape[0],axis = 0),axis = 0)
ball_classes = []
ball_capacity_classes = []
for i in range(n_classes):
ball_classes.append(np.matrix([a]))
ball_capacity_classes.append(np.matrix(0))
n_balls = 0
print("BALLS DUMPED!!")
#print(n_balls)
##print(x,y)
#print("This class has "+str(ball_capacity_classes[0].shape) + " balls")
#print("This class has "+str(ball_capacity_classes[1].shape) + " balls")
#print("This class has "+str(ball_capacity_classes[2].shape) + " balls")
# #print (ball_classes)
#******************************************************************************
for i in range(n_classes):
ball_classes[i] = ball_classes[i][1:]
#print(i)
if(classes.size == 0):
balls_pack = ball_classes[0]
classes = np.repeat(1,ball_classes[0].shape[0],axis = 0)
else :
classes = np.append(classes,np.repeat(1,ball_classes[0].shape[0],axis = 0))
balls_pack = np.append(balls_pack,ball_classes[0], axis=0)
for i in range(1,n_classes):
balls_pack = np.append(balls_pack,ball_classes[i],axis = 0)
classes = np.append(classes,np.repeat(i+1,ball_classes[i].shape[0],axis = 0),axis = 0)
ball_classes = []
ball_capacity_classes = []
for i in range(n_classes):
ball_classes.append(np.matrix([a]))
ball_capacity_classes.append(np.matrix(0))
#******************************************************************************
dump_svmlight_file(balls_pack, classes, "zeroBalls", zero_based=False)
#print(n_balls)
quit()
#print(newX.shape)
#print(newY.shape)
dump_svmlight_file(newX[1:], newY[1:], "newReduced", zero_based=False)
if __name__ == '__main__':
main()