C:\Users\admin\Desktop\pyLSA-master>python lsa.py
---- TESTS OF SUMMARIZATION ----
TASK - SUMMARIZE A DESCRIPTION OF THE GETTYSBURG ADDRESS:
Principal component analysis (PCA), based on information theory concepts, seeks a computational model that best describes a face by extracting the most relevant information contained in that face. The Eigenfaces approach is a PCA method, in which a small set of characteristic pictures are used to describe the variation between face images. The goal is to find the eigenvectors (eigenfaces) of the covariance matrix of the distribution, spanned by training a set of face images. Later, every face image is represented by a linear combination of these eigenvectors. Recognition is performed by projecting a new image onto the subspace spanned by the eigenfaces and then classifying the face by comparing its position in the face space with the positions of known individuals.
1 SENTENCE SUMMARY:
Traceback (most recent call last):
File "lsa.py", line 228, in
summary = summarize(query=string, k = 1)
File "lsa.py", line 106, in summarize
lsa1.parse(sentence)
File "lsa.py", line 154, in parse
w = w.lower().translate(None, self.ignore_characters)
TypeError: translate() takes exactly one argument (2 given)
C:\Users\admin\Desktop\pyLSA-master>python lsa.py
---- TESTS OF SUMMARIZATION ----
TASK - SUMMARIZE A DESCRIPTION OF THE GETTYSBURG ADDRESS:
Principal component analysis (PCA), based on information theory concepts, seeks a computational model that best describes a face by extracting the most relevant information contained in that face. The Eigenfaces approach is a PCA method, in which a small set of characteristic pictures are used to describe the variation between face images. The goal is to find the eigenvectors (eigenfaces) of the covariance matrix of the distribution, spanned by training a set of face images. Later, every face image is represented by a linear combination of these eigenvectors. Recognition is performed by projecting a new image onto the subspace spanned by the eigenfaces and then classifying the face by comparing its position in the face space with the positions of known individuals.
1 SENTENCE SUMMARY:
Traceback (most recent call last):
File "lsa.py", line 228, in
summary = summarize(query=string, k = 1)
File "lsa.py", line 106, in summarize
lsa1.parse(sentence)
File "lsa.py", line 154, in parse
w = w.lower().translate(None, self.ignore_characters)
TypeError: translate() takes exactly one argument (2 given)