Merge branch 'master' of https://github.com/ddbourgin/numpy-ml into master

Author: ddbourgin

numpy_ml/ngram/ngram.py

@@ -506,7 +506,7 @@ def train(self, corpus_fp, vocab=None, encoding=None):
  Specifies the text encoding for corpus. Common entries are 'utf-8',
  'utf-8-sig', 'utf-16'. Default is None.
  """
- self._train(corpus_fp, vocab=None, encoding=None)
+ self._train(corpus_fp, vocab=vocab, encoding=encoding)
  self._calc_smoothed_counts()
 
  def log_prob(self, words, N):

numpy_ml/plots/gmm_plots.py

@@ -84,7 +84,7 @@ def plot():
  # take best fit over 10 runs
  best_elbo = -np.inf
  for k in range(10):
- _G = GMM(C=n_classes, seed=i * 3)
+ _G = GMM(C=n_classes, seed=k * 3)
  ret = _G.fit(X, max_iter=100, verbose=False)
  while ret != 0:
  print("Components collapsed; Refitting")

numpy_ml/tests/test_preprocessing.py

@@ -10,7 +10,11 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.feature_extraction.text import TfidfVectorizer
 
-from librosa.core.time_frequency import fft_frequencies
+try:
+ from librosa.core.time_frequency import fft_frequencies
+except ImportError:
+ # for librosa >= 0.8.0
+ from librosa import fft_frequencies
 from librosa.feature import mfcc as lr_mfcc
 from librosa.util import frame
 from librosa.filters import mel

requirements-test.txt

@@ -7,7 +7,7 @@ tensorflow
 keras
 gym
 huffman
-librosa
+librosa>=0.8.0
 nltk
 hmmlearn
 tox