Usuń 'predict_lr.py'

predict_lr.py

@@ -1,36 +0,0 @@
-import pickle
-import sys
-import math
-import fileinput
-
-model = pickle.load(open("model.pkl", "rb"))
-word_index, vocabulary, weights, words_count = model
-
-def predict():
-    output = []
-    for line in fileinput.input(openhook=fileinput.hook_encoded("utf-8")):
-        line = line.rstrip()
-        fields = line.split('\t')
-        label = fields[0].strip()
-        document = fields[0]
-        terms = document.split(' ')
-        for term in terms:
-            if term in words_count:
-                words_count[term] += 1
-            else:
-                words_count[term] = 1
-        expected = weights[0]
-        for t in terms:
-            if t in vocabulary:
-                expected +=(words_count[t]/len(words_count)*(weights[word_index[t]]))
-        if expected > 0.9:
-            output.append(1)
-        else:
-            output.append(0)
-
-    with open("out.tsv", "w") as out:
-        for val in output:
-            out.write(str(val)+"\n")
-
-predict()
-