Correct formatting

README.md

@@ -40,7 +40,7 @@ cd ~/torch; bash install-deps;
 luarocks install rnn
 ```
 The Estimation model can be downloaded here and because of size constraints the Tracking model can be abtained by download from this link 
-[tracking_model.mat] (https://drive.google.com/open?id=0Bxkc5_D0JjpiZWx4eTU1d0hsVXc)
+[tracking_model.dat.gz](https://drive.google.com/open?id=1-BwlbbHykIV52c-SL1ofcppxZ5pTTXai)
 
 ## How to use:
 

VTR_Results.py

@@ -0,0 +1,142 @@
+from keras.models import model_from_json
+import numpy as np
+import csv
+import math
+
+model = model_from_json(open('model.json').read())
+model.load_weights('weights.h5')
+data_dir = ""
+X_test = np.load(data_dir+'VTR_test_X.npy')
+Y = np.load(data_dir+'VTR_test_Y.npy')
+
+names = Y[:, :1]
+Y_test = Y[:,1:]
+predictions = []
+
+loss1 = 0.0
+loss2 = 0.0
+loss3 = 0.0
+loss4 = 0.0
+max_1 = 0.0
+max_2 = 0.0
+max_3 = 0.0
+max_4 = 0.0
+list_1 = []
+list_2 = []
+list_3 = []
+list_4 = []
+male = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
+female = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
+karma_list = [0, 0.0, 0.0, 0.0, 0.0]
+AVG_list = [0, 0.0, 0.0, 0.0, 0.0]
+
+y_hat = model.predict(X_test)
+for i in range(0,len(Y_test)):
+    l1 = np.abs(float(Y_test[i, 0]) - y_hat[i, 0])
+    l2 = np.abs(float(Y_test[i, 1]) - y_hat[i, 1])
+    l3 = np.abs(float(Y_test[i, 2]) - y_hat[i, 2])
+    l4 = np.abs(float(Y_test[i, 3]) - y_hat[i, 3])
+    pred = [names[i][0], float(Y_test[i, 0]), float(Y_test[i, 1]), float(Y_test[i, 2]), float(Y_test[i, 3])]
+
+    AVG_list[0] += 1
+    AVG_list[1] += float(Y_test[i, 0]) - y_hat[i, 0]
+    AVG_list[2] += float(Y_test[i, 1]) - y_hat[i, 1]
+    AVG_list[3] += float(Y_test[i, 2]) - y_hat[i, 2]
+    AVG_list[4] += float(Y_test[i, 3]) - y_hat[i, 3]
+
+    pred.extend([y_hat[i, 0], y_hat[i, 1], y_hat[i, 2], y_hat[i, 3]])
+
+    if names[i][0].split('_')[3][0] == 'f':
+        female[0] += 1
+        female[1] += l1
+        female[2] += l2
+        female[3] += l3
+        female[4] += l4
+        female[5].append(l1)
+        female[6].append(l2)
+        female[7].append(l3)
+        female[8].append(l4)
+    elif names[i][0].split('_')[3][0] == 'm':
+        male[0] += 1
+        male[1] += l1
+        male[2] += l2
+        male[3] += l3
+        male[4] += l4
+        male[5].append(l1)
+        male[6].append(l2)
+        male[7].append(l3)
+        male[8].append(l4)
+
+    predictions.append(pred)
+
+    list_1.append(l1)
+    list_2.append(l2)
+    list_3.append(l3)
+    list_4.append(l4)
+    max_1 = max(max_1,l1)
+    max_2 = max(max_2,l2)
+    max_3 = max(max_3,l3)
+    max_4 = max(max_4,l4)
+
+    loss1 += l1
+    loss2 += l2
+    loss3 += l3
+    loss4 += l4
+
+    karma_list[0] += 1
+    karma_list[1] += l1 * l1
+    karma_list[2] += l2 * l2
+    karma_list[3] += l3 * l3
+    karma_list[4] += l4 * l4
+loss1 /= len(Y_test)
+loss2 /= len(Y_test)
+loss3 /= len(Y_test)
+loss4 /= len(Y_test)
+total_loss = loss1+loss2+loss3+loss4
+total_loss /= 4.0
+print('standard deviation', round(np.std(list_1)*1000, 2), round(np.std(list_2)*1000, 2), round(np.std(list_3)*1000, 2), round(np.std(list_4)*1000, 2))
+print('median', round(np.median(list_1)*1000, 2), round(np.median(list_2)*1000, 2), round(np.median(list_3)*1000, 2), round(np.median(list_4)*1000, 2))
+print('max loss ', round(max_1*1000, 2), round(max_2*1000, 2), round(max_3*1000, 2), round(max_4*1000, 2))
+print('total loss ', round(total_loss*1000, 2))
+print('Real test score:', round(loss1*1000, 2), round(loss2*1000, 2), round(loss3*1000, 2), round(loss4*1000, 2))
+
+female[1] = round((female[1] / female[0])*1000, 2)
+female[2] = round((female[2] / female[0])*1000, 2)
+female[3] = round((female[3] / female[0])*1000, 2)
+female[4] = round((female[4] / female[0])*1000, 2)
+female[5] = round(np.std(female[5])*1000, 2)
+female[6] = round(np.std(female[6])*1000, 2)
+female[7] = round(np.std(female[7])*1000, 2)
+female[8] = round(np.std(female[8])*1000, 2)
+
+male[1] = round((male[1] / male[0])*1000, 2)
+male[2] = round((male[2] / male[0])*1000, 2)
+male[3] = round((male[3] / male[0])*1000, 2)
+male[4] = round((male[4] / male[0])*1000, 2)
+male[5] = round(np.std(male[5])*1000, 2)
+male[6] = round(np.std(male[6])*1000, 2)
+male[7] = round(np.std(male[7])*1000, 2)
+male[8] = round(np.std(male[8])*1000, 2)
+
+print("male: ", male)
+print("female: ", female)
+
+# karma
+
+karma_list[1] /= karma_list[0]
+karma_list[2] /= karma_list[0]
+karma_list[3] /= karma_list[0]
+karma_list[4] /= karma_list[0]
+print('root mean squared error ', round(math.sqrt(karma_list[1]) * 1000, 2), round(math.sqrt(karma_list[2]) * 1000, 2),
+                            round(math.sqrt(karma_list[3]) * 1000, 2), round(math.sqrt(karma_list[4]) * 1000, 2))
+
+AVG_list[1] /= AVG_list[0]
+AVG_list[2] /= AVG_list[0]
+AVG_list[3] /= AVG_list[0]
+AVG_list[4] /= AVG_list[0]
+print('AVG ', round(AVG_list[1] * 1000, 2), round(AVG_list[2] * 1000, 2), round(AVG_list[3] * 1000, 2), round(AVG_list[4] * 1000, 2))
+
+
+with open("results/VTR.csv", "wb") as f:
+    writer = csv.writer(f)
+    writer.writerows(predictions)

extract_features.py

@@ -26,7 +26,8 @@ def build_data(wav,begin=None,end=None):
     dstr = wav_in_file.readframes(N)
     data = np.fromstring(dstr, np.int16)
     if begin is not None and end is not None:
-        return data[begin*16000:end*16000]
+        #return data[begin*16000:end*16000] #numpy 1.11.0
+        return data[np.int(begin*16000):np.int(end*16000)] #numpy 1.14.0
     X = []
     l = len(data)
     for i in range(0, l-100, 160):