word2vec analogy test (#63)

* Semantic Similarity Dataset

* change format

* rename folder

* word2vec analogy test

* minor change

* readme added

code/analogy/anology_test.py

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+import gensim
+from scipy.spatial.distance import cosine, euclidean
+import  numpy as np
+model = gensim.models.Word2Vec.load("w2v_farsi.model")
+
+data=[]
+groundTruth=[]
+with open("data.csv","r",encoding="UTF-8")as f:
+    for line in f:
+        row = []
+        line=line.split(",")
+        row.append(line[1])
+        row.append(line[2])
+        row.append(line[3])
+        groundTruth.append(line[4])
+        data.append(row)
+
+j=0
+tp=0
+for row in data:
+    i=0
+    for w in row:
+        try:
+            if i==0:
+                A=model.wv[w]
+            if i==1:
+                B=model.wv[w]
+            if i==2:
+                C=model.wv[w]
+            i+=1
+        except:
+            if  i==0:
+                A=0
+            if i==1:
+                B=0
+            if  i==2:
+                C=0
+            i+=1
+
+    if (A is not 0) and (B is not 0)and(C is not 0):
+        mi=np.subtract(B,A)
+        s=np.add(mi,C)
+        most_similars=model.similar_by_vector(s,topn=10)
+
+        for word in most_similars:
+            if (groundTruth[j].strip() is word[0].strip()):
+                tp+=1
+
+    j+=1
+
+
+print('accuracy = ', tp/len(data))

results/word2vec-analogy/README.md

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+**The result of analogy test on word2vec model**
+The model was trained on Wikipedia.
+The test was ran on analogy.csv which contain 19716 group.
+The accuracy is **0.27429498884155**