git push!

mian.py

@@ -1,5 +1,3 @@
-import csv
-
 import nltk
 import pandas as pd
 from sklearn.neural_network import MLPClassifier
@@ -88,3 +86,53 @@ print(przewidywania)
 with open("test-A/out.tsv", "w", encoding="utf-8") as uwu:
     for p in przewidywania:
         uwu.write(str(p)+"\n")
+
+### dev-0
+
+# w pliku in.tsv w kolumnach 1983, 5199 trzeba zamienic w tekscie tabulator na 4 spacje
+dev_in = pd.read_csv('dev-0/in.tsv', sep='\t')
+dev_in.columns = ["x"]
+
+print(dev_in["x"][0])
+
+dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t')
+dev_expected.columns = ["y"]
+
+print(dev_expected["y"][0])
+
+# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
+slowa = []
+for tekst in dev_in["x"]:
+    pom = []
+    for slowo in word_tokenize(tekst):
+        pom.append(slowo.lower())
+    slowa.append(pom)
+print(slowa[0])
+
+teksty = []
+for tekst in test["x"]:
+    pom = None
+    for slowo in word_tokenize(tekst):
+        wektor = None
+        try:
+            wektor = model.wv[slowo.lower()]
+        except KeyError:
+            pass
+        if wektor is not None:
+            if pom is None:
+                pom = wektor
+            else:
+                pom = pom + wektor
+    teksty.append(wektor)
+print(teksty[0])
+
+przewidywania = clf.predict(teksty)
+print(przewidywania)
+
+with open("dev-0/out.tsv", "w", encoding="utf-8") as uwu:
+    for p in przewidywania:
+        uwu.write(str(p)+"\n")
+
+for i in range(len(przewidywania)):
+    print(przewidywania[i], dev_expected["y"][i])
+

test-A/out.tsv

@@ -3796,7 +3796,7 @@
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@@ -5174,7 +5174,7 @@
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