<commit_before>import time

import pickle
import matplotlib.pyplot as plt
import numpy as np

import utils
from network_loader import SeizureData
from relabeling_functions import relabel,reorder
from extrator import FeatureExtractor
from classifier import NetworkClassifer
from make_pdfs import plot_traces

################# Training Data ###################
reload_training = True
if reload_training:
    training_traces = utils.raw_training_load()
    training_traces_norm = utils.normalise(training_traces)
    training_data = FeatureExtractor(training_traces_norm)
    #f = open('../full_raw_training','wb')
    #pickle.dump(training_traces,f)

elif not reload_training:
    print 'skipping raw training load'
    training_traces = pickle.load(open('../full_raw_training','rb'))
    training_traces_norm = utils.normalise(training_traces)
    training_data = FeatureExtractor(training_traces_norm)
    np.savetxt('training_traces.csv',training_traces_norm,delimiter=',')

################# Training Labels and mixed event exclusion ###################
cleanup = np.loadtxt('../Training_cleanup.csv',delimiter=',')
training_labels = np.array([int(x[1]) for x in cleanup])
print training_labels.shape
training_indexes = []
for i in range(training_labels.shape[0]):
        if training_labels[i] != 0:
            training_indexes.append(i)

################## Test Data ####################
reload_validation = True
if reload_validation:
    validation_traces = utils.raw_validation_load()
    validation_traces_norm = utils.normalise(validation_traces)
    validation_data = FeatureExtractor(validation_traces_norm)
    #f = open('../raw_validation','wb')
    #pickle.dump(validation_traces,f)

elif not reload_training:
    print 'skipping raw validation load'
    validation_traces = pickle.load(open('../raw_validation','rb'))
    validation_traces_norm = utils.normalise(validation_traces)
    validation_data = FeatureExtractor(validation_traces_norm)
    np.savetxt('test_traces.csv',validation_traces_norm,delimiter=',')

################# Validation cleanup ###################
cleanup = np.loadtxt('../validation_cleanUp.csv',delimiter=',')
validation_labels = np.array([int(x[1]) for x in cleanup])

validation_indexes = []
for i in range(validation_labels.shape[0]):
        if validation_labels[i] != 0:
            validation_indexes.append(i)

np.savetxt('all_traces.csv',np.vstack((validation_traces_norm,training_traces_norm)),delimiter=',')
np.savetxt('all_cleanup.csv',np.hstack((validation_labels)), delimiter = ',')
classifier = NetworkClassifer(training_data.feature_array[training_indexes,:],training_labels[training_indexes],<commit_msg>network prediction: includes lda and color coded output pdfs<commit_after>import time

import pickle
import matplotlib.pyplot as plt
import numpy as np

import utils
from network_loader import SeizureData
from relabeling_functions import relabel,reorder
from extrator import FeatureExtractor
from classifier import NetworkClassifer
from make_pdfs import plot_traces

################# Training Data ###################
reload_training = False
if reload_training:
    training_traces = utils.raw_training_load()
    training_traces_norm = utils.normalise(training_traces)
    training_data = FeatureExtractor(training_traces_norm)
    #f = open('../full_raw_training','wb')
    #pickle.dump(training_traces,f)

elif not reload_training:
    print 'skipping raw training load'
    training_traces = pickle.load(open('../full_raw_training','rb'))
    training_traces_norm = utils.normalise(training_traces)
    training_data = FeatureExtractor(training_traces_norm)
    np.savetxt('training_traces.csv',training_traces_norm,delimiter=',')

################# Training Labels and mixed event exclusion ###################
cleanup = np.loadtxt('../Training_cleanup.csv',delimiter=',')
training_labels = np.array([int(x[1]) for x in cleanup])
print training_labels.shape
training_indexes = []
for i in range(training_labels.shape[0]):
        if training_labels[i] != 0:
            training_indexes.append(i)

################## Test Data ####################
reload_validation = False
if reload_validation:
    validation_traces = utils.raw_validation_load()
    validation_traces_norm = utils.normalise(validation_traces)
    validation_data = FeatureExtractor(validation_traces_norm)
    #f = open('../raw_validation','wb')
    #pickle.dump(validation_traces,f)

elif not reload_training:
    print 'skipping raw validation load'
    validation_traces = pickle.load(open('../raw_validation','rb'))
    validation_traces_norm = utils.normalise(validation_traces)
    validation_data = FeatureExtractor(validation_traces_norm)
    np.savetxt('test_traces.csv',validation_traces_norm,delimiter=',')

################# Validation cleanup ###################
cleanup = np.loadtxt('../validation_cleanUp.csv',delimiter=',')
validation_labels = np.array([int(x[1]) for x in cleanup])

validation_indexes = []
for i in range(validation_labels.shape[0]):
        if validation_labels[i] != 0:
            validation_indexes.append(i)

np.savetxt('all_traces.csv',np.vstack((validation_traces_norm,training_traces_norm)),delimiter=',')
np.savetxt('all_cleanup.csv',np.hstack((validation_labels)), delimiter = ',')
classifier = NetworkClassifer(training_data.feature_array[training_indexes,:],training_labels[training_indexes],
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