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Berkeley.txt

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+jumping in place
+jumping jacks
+bending
+punching
+waving two hands
+waving one hand
+clapping hands
+throwing a ball
+sit down then stand up
+sit down
+stand up

Glove.py

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+from gensim.scripts.glove2word2vec import glove2word2vec
+from gensim.models import KeyedVectors
+import gensim
+import numpy as np
+
+UTD_Class = []
+MMAct_Class = []
+Berkeley_Class = []
+
+for line in open("UTD.txt","r"): #设置文件对象并读取每一行文件
+    UTD_Class.append(line[:-1])  
+for line in open("MMAct.txt","r"): #设置文件对象并读取每一行文件
+    MMAct_Class.append(line[:-1]) 
+for line in open("Berkeley.txt","r"): #设置文件对象并读取每一行文件
+    Berkeley_Class.append(line[:-1]) 
+
+glove_input_file = "glove.840B.300d.txt"
+word2vec_output_file="glove.840B.300d.word2vec.txt"
+(count, dimensions) = glove2word2vec(glove_input_file, word2vec_output_file)
+print(count, '\n', dimensions)
+
+glove_model = KeyedVectors.load_word2vec_format(word2vec_output_file,binary=False)
+
+
+UTD_vec=np.zeros(shape=[1,300],dtype=float) 
+for i in range(0,len(UTD_Class)):
+    UTD_vec_data=np.zeros((1,300),dtype=float)
+    for data in UTD_Class[i].split():
+        UTD_vec_data=UTD_vec_data+glove_model[data]
+    #UTD_vec_data=UTD_vec_data/len(UTD_Class[i].split())   
+    UTD_vec=np.vstack((UTD_vec,UTD_vec_data))
+UTD_vec=UTD_vec[1:,:]
+np.save('UTD_Glove.npy',UTD_vec)
+
+
+
+MMAct_vec=np.zeros(shape=[1,300],dtype=float) 
+for i in range(0,len(MMAct_Class)):
+    MMAct_vec_data=np.zeros((1,300),dtype=float)
+    for data in MMAct_Class[i].split():
+        MMAct_vec_data=MMAct_vec_data+glove_model[data]
+    #MMAct_vec_data=MMAct_vec_data/len(MMAct_Class[i].split())
+    MMAct_vec=np.vstack((MMAct_vec,MMAct_vec_data))
+MMAct_vec=MMAct_vec[1:,:]
+np.save('MMAct_Glove.npy',MMAct_vec)
+
+
+Berkeley_vec=np.zeros(shape=[1,300],dtype=float) 
+for i in range(0,len(Berkeley_Class)):
+    Berkeley_vec_data=np.zeros((1,300),dtype=float)
+    for data in Berkeley_Class[i].split():
+        Berkeley_vec_data=Berkeley_vec_data+glove_model[data]
+    #Berkeley_vec_data=Berkeley_vec_data/len(Berkeley_Class[i].split())
+    Berkeley_vec=np.vstack((Berkeley_vec,Berkeley_vec_data))
+Berkeley_vec=Berkeley_vec[1:,:]
+np.save('Berkeley_Glove.npy',Berkeley_vec)    

MMAct.txt

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+carrying
+carrying heavy
+carrying light
+checking time
+closing
+crouching
+drinking
+entering
+exiting
+fall
+jumping
+kicking
+loitering
+looking around
+object transfering
+opening
+picking up
+pocket in
+pocket out
+pointing
+pulling
+pushing
+running
+setting down
+sitting
+sitting down
+standing
+standing up
+talking
+talking on phone
+throwing
+transferring large object
+using pc
+using phone
+walking
+waving hand

UTD.txt

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+right arm swipe to the left
+right arm swipe to the right
+right hand wave
+two hand front clap
+right arm throw
+cross arms in the chest 
+basketball shoot 
+right hand draw x 
+right hand draw circle clockwise
+right hand draw circle counter clockwise
+draw triangle 
+bowling right hand
+front box
+baseball swing from right 
+tennis right hand forehand swing 
+arm curl two arms
+tennis serve
+two hand push 
+right hand knock on door
+right hand catch an object
+right hand pick up and throw 
+jogging in place
+walking in place 
+sit to stand 
+stand to sit 
+forward lunge left foot forward
+squat two arms stretch out