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import numpy as np
import sys
import os
import pickle, gzip
from scipy.special import expit
emotionLabels = pickle.load(gzip.open('LabelNumMap.pickle.gz', 'rb'), encoding='latin1')
fileidx = pickle.load(gzip.open('testIdx.pickle.gz', 'rb'), encoding='latin1')
feature_mat = pickle.load(gzip.open('testfeature.pickle.gz', 'rb'), encoding='latin1')
_, indices = pickle.load(gzip.open('test.pickle.gz', 'rb'), encoding='latin1')
W, U = pickle.load(gzip.open('ELMWeights.pickle.gz', 'rb'), encoding='latin1')
segnum = indices.max()
utterFeatList = []
for i in range(segnum + 1):
frames = feature_mat[indices == i, :]
# change the way of computing energy using the new energy file
if frames.size != 0:
feat1 = np.amax(frames, axis=0)
feat2 = np.amin(frames, axis=0)
feat3 = np.mean(frames, axis=0)
feat4 = np.mean(frames > 0.2, axis=0)
utter_feat = np.hstack([feat1, feat2, feat3, feat4])
utterFeatList.append(utter_feat)
utterFeats = np.vstack(utterFeatList)
H = expit(W.T.dot(utterFeats.T))
Predicts = H.T.dot(U)
results = np.argmax(Predicts, axis= 1)
inv_map = {v: k for k, v in emotionLabels.items()}
text_file = open("Results.txt", "w")
for i in range(segnum + 1):
text_file.write("%s %s\n" % (fileidx[i], inv_map[results[i]]))
text_file.close()