upd

llm/simple_models.py

@@ -0,0 +1,97 @@
+# Bag of words models and TFIDF
+
+from bs4 import BeautifulSoup
+import requests
+import re
+
+response = requests.get("https://github.com/maxvonlancaster")
+root = BeautifulSoup(response.content, "lxml")
+txt = root.text
+from wordcloud import WordCloud
+wc = WordCloud(width=800,height=400).generate(re.sub(r"\s+"," ", root.text))
+wc.to_image()
+wc.to_file('wc.png')
+
+# ####################################################################################################
+
+
+# Term frequency
+
+documents = ["the goal of this lecture is to explain the basics of free text processing",
+             "the bag of words model is one such approach",
+             "text processing via bag of words"]
+
+# documents = ["the goal of this lecture is to explain the basics of free text processing",
+#              "the bag of words model is one such approach",
+#              "tralalalal text text tralall"]
+
+
+document_words = [doc.split() for doc in documents]
+vocab = sorted(set(sum(document_words, [])))
+vocab_dict = {k:i for i,k in enumerate(vocab)}
+print(vocab, "\n")
+print(vocab_dict, "\n")
+
+# # Now let’s construct a matrix that contains word counts (term frequencies) for all the documents
+
+import numpy as np
+X_tf = np.zeros((len(documents), len(vocab)), dtype=int)
+for i,doc in enumerate(document_words):
+    for word in doc:
+        X_tf[i, vocab_dict[word]] += 1
+print(X_tf)
+
+# ####################################################################################################
+
+
+# Inverse document frequency
+# idf = log(documents / documents with word)
+
+idf = np.log(X_tf.shape[0]/X_tf.astype(bool).sum(axis=0))
+print(idf)
+
+# ####################################################################################################
+
+
+# # TFIDF
+
+X_tfidf = X_tf * idf
+print("TFIDF ", X_tfidf)
+
+# ####################################################################################################
+
+
+# # Cosine similarity
+
+X_tfidf_norm = X_tfidf / np.linalg.norm(X_tfidf, axis=1)[:,None]
+M = X_tfidf_norm @ X_tfidf_norm.T
+print(M)
+
+
+# # Word embeddings and word2vec
+
+# documents = [
+#     "pittsburgh has some excellent new restaurants",
+#     "boston is a city with great cuisine",
+#     "postgresql is a relational database management system"
+# ]
+
+# import gensim as gs
+# import gensim.downloader as api
+# import numpy as np
+# model = gs.models.KeyedVectors.load_word2vec_format('lee_background.cor', binary=True)
+# # model = api.load('word2vec-google-news-300')
+# print(model.wv["pittsburgh"][:10])
+
+from gensim.test.utils import lee_corpus_list
+from gensim.models import Word2Vec
+
+model = Word2Vec(lee_corpus_list, vector_size=24, epochs=100)
+word_vectors = model.wv
+print(word_vectors["has"])
+
+print(model.wv.most_similar("has"))
+
+
+
+

nn/basic_example.py

@@ -127,4 +127,7 @@ def train(self, data, all_y_trues):
 
 # Train our neural network!
 network = OurNeuralNetwork()
-network.train(data, all_y_trues)
+network.train(data, all_y_trues)
+
+output = network.feedforward([-35, -10])
+print(output)