from pylab import * #ALWAYS IMPORT PYLAB BEFORE THE OTHER LIBRARIES
import subprocess, sys, time, random, math
from numpy import *
from knn import *
from matplotlib.pyplot import *
from load_shrinked import load_dataset
''' Fourier descriptors not working as it should do, so dont use it
(at least not until a fixed implementation is submitted) '''
percent_dataset_usage = 1
feature = 'input_space' #'input_space' or 'fds' - Use input_space
ks = [1, 3] #k-values (classifiers) - multiple values
'''Read shrinked dataset'''
train, valid, test, traint, validt, testt = load_dataset()
#concatenate train, valid sets
train = np.concatenate((train, valid), axis=0)
traint = np.concatenate((traint, validt), axis=0)
#Sampling a subset of dataset
train = train[:percent_dataset_usage * 60000 / 100]
traint = traint[:percent_dataset_usage * 60000 / 100]
start = time.time()
if feature == 'fds':
'''train set'''
contours_train = []
for i in range(0, shape(train)[0]):
pic = 255 * train[i]
contours_train.append(
#import matplotlib.pyplot as plt
#import mpl_toolkits.mplot3d.axes3d as p3
#import matplotlib.dates as dates
PERCENT_DATASET_USED = 23
def print_time_elapsed(start):
end = time.time()
seconds = end - start
minutes = math.floor(seconds / 60)
secs = seconds % 60
print 'time elapsed: ' + str(minutes) + 'min ' + str(secs) + 's' + '\n\n'
train_set, valid_set, test_set, traint_init, validt_init, testt = load_dataset(
)
#Concatanate train, valid sets
train_set = np.concatenate((train_set, valid_set), axis=0)
traint = np.concatenate((traint_init, validt_init), axis=0)
#Reshape X from NX14x14 to NX196 , Y remains Nx1
train = np.zeros((
np.shape(train_set)[0],
196,
))
for i in range(0, np.shape(train)[0]):
train[i] = train_set[i].reshape(196, )
test = np.zeros((
np.shape(test_set)[0],
196,
#import matplotlib.pyplot as plt
#import mpl_toolkits.mplot3d.axes3d as p3
#import matplotlib.dates as dates
PERCENT_DATASET_USED = 23
def print_time_elapsed(start):
end = time.time()
seconds = end-start
minutes = math.floor(seconds / 60)
secs = seconds % 60
print 'time elapsed: ' +str(minutes) + 'min ' +str(secs) +'s' + '\n\n'
train_set, valid_set, test_set, traint_init, validt_init, testt = load_dataset()
#Concatanate train, valid sets
train_set = np.concatenate((train_set, valid_set), axis=0)
traint = np.concatenate((traint_init, validt_init), axis=0)
#Reshape X from NX14x14 to NX196 , Y remains Nx1
train = np.zeros((np.shape(train_set)[0], 196,))
for i in range(0, np.shape(train)[0]):
train[i] = train_set[i].reshape(196,)
test = np.zeros((np.shape(test_set)[0], 196,))
for i in range(0, np.shape(test)[0]):
test[i] = test_set[i].reshape(196,)
print np.shape(train), np.shape(traint)
print np.shape(test), np.shape(testt)
from pylab import * # ALWAYS IMPORT PYLAB BEFORE THE OTHER LIBRARIES
import subprocess, sys, time, random, math
from numpy import *
from knn import *
from matplotlib.pyplot import *
from load_shrinked import load_dataset
""" Fourier descriptors not working as it should do, so dont use it
(at least not until a fixed implementation is submitted) """
percent_dataset_usage = 1
feature = "input_space" #'input_space' or 'fds' - Use input_space
ks = [1, 3] # k-values (classifiers) - multiple values
"""Read shrinked dataset"""
train, valid, test, traint, validt, testt = load_dataset()
# concatenate train, valid sets
train = np.concatenate((train, valid), axis=0)
traint = np.concatenate((traint, validt), axis=0)
# Sampling a subset of dataset
train = train[: percent_dataset_usage * 60000 / 100]
traint = traint[: percent_dataset_usage * 60000 / 100]
start = time.time()
if feature == "fds":
"""train set"""
