def test_classify_2016(self):
model = train('./corpus/training/', 2)
classification = classify(model, './corpus/test/2016/19.txt')
expected_classification = {
'log p(y=2016|x)': -3800.4027665365134,
'log p(y=2020|x)': -3805.776535552692,
'predicted y': '2016'
}
self.compare_dicts(classification, expected_classification)
def test_classify_2020(self):
model = train('./corpus/training/', 2)
classification = classify(model, './corpus/test/2016/0.txt')
expected_classification = {
'log p(y=2020|x)': -3906.351945884105,
'log p(y=2016|x)': -3916.458747858926,
'predicted y': '2020'
}
self.compare_dicts(classification, expected_classification)
def test_train(self):
model = train('./EasyFiles/', 2)
expected_model = {
'vocabulary': ['.', 'a'],
'log prior': {
'2020': -0.916290731874155,
'2016': -0.5108256237659905
},
'log p(w|y=2020)': {
'.': -1.6094379124341005,
'a': -2.302585092994046,
None: -0.35667494393873267
},
'log p(w|y=2016)': {
'.': -1.7047480922384253,
'a': -1.2992829841302609,
None: -0.6061358035703157
}
}
self.compare_dicts(model, expected_model)
def train_with_feature_set(self, feature_set, pred_blobs, real_blobs, feat_weight=False):
X, Y = classify.create_training_set_from_feature_set(feature_set, pred_blobs, real_blobs)
return classify.train(X, Y, self.clf, self.scaler, self.selector, feat_weight)
import serialread
import preprocessing
import feature
import classify
import sys
import socket
import numpy as np
import time
import bluetooth
from scipy import signal
trained = classify.train()
bt_address = "20:16:08:04:80:97" # MAC address of our emotion recognition hardware.
bt_port = 1
bt = bluetooth.BluetoothSocket(bluetooth.RFCOMM)
print("Establishing Bluetooth connection...")
bt.connect((bt_address, bt_port))
print("Bluetooth connection established.")
print("Calibrating for your relaxed state. Please wait...")
time.sleep(5)
std_features_r = [0]
# Extract features for 50 seconds when user is relaxed and uses the means and
# standard deviations of the extracted, relaxed state features for normalization later on.
while 0 in std_features_r:
features_r = []