def main():
"""
"""
import random
import numpy
model = createModel()
shifter = InferenceShifter()
tobological_data, label = load_dataset(
'./data/pylearn2_gcn_whitened/train.pkl')
for i, data in enumerate(tobological_data[:1000]):
if label[i][0] not in (0, 1):
continue
patch_data, movement = get_patch(data,
height=patch_heigh,
width=patch_width,
step=patch_step)
print '%d, label:%s, ' % (i, label[i][0]),
for data in patch_data:
input_len = reduce(lambda x, y: x * y, data.shape)
input_data = {
'pixel': data.reshape((input_len)).tolist(),
'label': label[i][0]
}
model.enableLearning()
result = model.run(input_data)
#result = shifter.shift(result)
print label[i][0], result.inferences['multiStepBestPredictions']
#model.resetSequenceStates()
#model._getTPRegion().executeCommand(['resetSequenceStates'])
#model._getTPRegion().resetSequenceStates()
model._getTPRegion().getSelf().resetSequenceStates()
# validate
if i % 3 == 0:
valid = validate(model, test_data, test_label, limit=30)
print '%d : valid: %8.5f' % (i, valid)
def main():
"""
"""
import random
import numpy
model = createModel()
shifter = InferenceShifter()
tobological_data, label = load_dataset('./data/pylearn2_gcn_whitened/train.pkl')
for i, data in enumerate(tobological_data[:1000]):
if label[i][0] not in (0, 1):
continue
patch_data, movement = get_patch(data, height=patch_heigh, width=patch_width, step=patch_step)
print '%d, label:%s, ' % (i, label[i][0]),
for data in patch_data:
input_len = reduce(lambda x,y: x * y, data.shape)
input_data = {
'pixel': data.reshape((input_len)).tolist() ,
'label': label[i][0]
}
model.enableLearning()
result = model.run(input_data)
#result = shifter.shift(result)
print label[i][0], result.inferences['multiStepBestPredictions']
#model.resetSequenceStates()
#model._getTPRegion().executeCommand(['resetSequenceStates'])
#model._getTPRegion().resetSequenceStates()
model._getTPRegion().getSelf().resetSequenceStates()
# validate
if i % 3 == 0:
valid = validate(model, test_data, test_label, limit=30)
print '%d : valid: %8.5f' % (i, valid)
# /usr/bin/python
# coding: utf-8
from pprint import pprint
from pylab import *
from collections import defaultdict, Counter
from nupic_dir.lib.cla_classifier import ClaClassifier
from nupic_dir.lib.function_data import function_data
from nupic_dir.lib.plotter import Plotter
from nupic_dir.lib.create_network import net_structure, sensor_params, dest_resgion_data, class_encoder_params
from nupic_dir.lib.load_data import load_dataset, get_patch
test_data, test_label = load_dataset("./data/pylearn2_gcn_whitened/test.pkl")
train_data, train_label = load_dataset("./data/pylearn2_gcn_whitened/train.pkl")
patch_heigh = 32
patch_width = 32
patch_step = 32
def validate(recogniter, test_data, test_label, limit=100):
result = []
tdata = test_data[:limit]
for i, data in enumerate(tdata):
patch_result = Counter()
patch_data, movement = get_patch(data, height=patch_heigh, width=patch_width, step=patch_step)
for patch in patch_data:
input_len = reduce(lambda x, y: x * y, patch.shape)
input_data = {"pixel": patch.reshape((input_len)).tolist(), "label": "no"}
#/usr/bin/python
# coding: utf-8
from pprint import pprint
from pylab import *
from collections import defaultdict, Counter
from nupic_dir.lib.cla_classifier import ClaClassifier
from nupic_dir.lib.function_data import function_data
from nupic_dir.lib.plotter import Plotter
from nupic_dir.lib.create_network import net_structure, sensor_params, dest_resgion_data, class_encoder_params
from nupic_dir.lib.load_data import load_dataset, get_patch
test_data, test_label = load_dataset('./data/pylearn2_gcn_whitened/test.pkl')
train_data, train_label = load_dataset(
'./data/pylearn2_gcn_whitened/train.pkl')
patch_heigh = 32
patch_width = 32
patch_step = 32
def validate(recogniter, test_data, test_label, limit=100):
result = []
tdata = test_data[:limit]
for i, data in enumerate(tdata):
patch_result = Counter()
patch_data, movement = get_patch(data,
height=patch_heigh,
width=patch_width,
step=patch_step)
