Updated depreciated np.int too np.int_

requirements.txt

@@ -2,5 +2,5 @@ numpy
 pytest
 pytest-cov
 codecov
-sklearn
+scikit-learn
 scipy

saxpy/alphabet.py

@@ -1,98 +1,36 @@
 """Implements Alphabet cuts."""
 import numpy as np
-
+import scipy
 
 def cuts_for_asize(a_size):
- """Generate a set of alphabet cuts for its size."""
- """ Typically, we generate cuts in R as follows:
+ """Generate a set of alphabet cuts for its size.
+
+ Typically, we generate cuts in R as follows:
  get_cuts_for_num <- function(num) {
- cuts = c(-Inf)
- for (i in 1:(num-1)) {
- cuts = c(cuts, qnorm(i * 1/num))
- }
- cuts
+  cuts = c(-Inf)
+  for (i in 1:(num-1)) {
+  cuts = c(cuts, qnorm(i * 1/num))
+  }
+  cuts
  }
 
- get_cuts_for_num(3) """
- options = {
- 2: np.array([-np.inf, 0.00]),
- 3: np.array([-np.inf, -0.4307273, 0.4307273]),
- 4: np.array([-np.inf, -0.6744898, 0, 0.6744898]),
- 5: np.array([-np.inf, -0.841621233572914, -0.2533471031358,
- 0.2533471031358, 0.841621233572914]),
- 6: np.array([-np.inf, -0.967421566101701, -0.430727299295457, 0,
- 0.430727299295457, 0.967421566101701]),
- 7: np.array([-np.inf, -1.06757052387814, -0.565948821932863,
- -0.180012369792705, 0.180012369792705, 0.565948821932863,
- 1.06757052387814]),
- 8: np.array([-np.inf, -1.15034938037601, -0.674489750196082,
- -0.318639363964375, 0, 0.318639363964375,
- 0.674489750196082, 1.15034938037601]),
- 9: np.array([-np.inf, -1.22064034884735, -0.764709673786387,
- -0.430727299295457, -0.139710298881862, 0.139710298881862,
- 0.430727299295457, 0.764709673786387, 1.22064034884735]),
- 10: np.array([-np.inf, -1.2815515655446, -0.841621233572914,
- -0.524400512708041, -0.2533471031358, 0, 0.2533471031358,
- 0.524400512708041, 0.841621233572914, 1.2815515655446]),
- 11: np.array([-np.inf, -1.33517773611894, -0.908457868537385,
- -0.604585346583237, -0.348755695517045,
- -0.114185294321428, 0.114185294321428, 0.348755695517045,
- 0.604585346583237, 0.908457868537385, 1.33517773611894]),
- 12: np.array([-np.inf, -1.38299412710064, -0.967421566101701,
- -0.674489750196082, -0.430727299295457,
- -0.210428394247925, 0, 0.210428394247925,
- 0.430727299295457, 0.674489750196082, 0.967421566101701,
- 1.38299412710064]),
- 13: np.array([-np.inf, -1.42607687227285, -1.0200762327862,
- -0.736315917376129, -0.502402223373355,
- -0.293381232121193, -0.0965586152896391,
- 0.0965586152896394, 0.293381232121194, 0.502402223373355,
- 0.73631591737613, 1.0200762327862, 1.42607687227285]),
- 14: np.array([-np.inf, -1.46523379268552, -1.06757052387814,
- -0.791638607743375, -0.565948821932863, -0.36610635680057,
- -0.180012369792705, 0, 0.180012369792705,
- 0.36610635680057, 0.565948821932863, 0.791638607743375,
- 1.06757052387814, 1.46523379268552]),
- 15: np.array([-np.inf, -1.50108594604402, -1.11077161663679,
- -0.841621233572914, -0.622925723210088,
- -0.430727299295457, -0.2533471031358, -0.0836517339071291,
- 0.0836517339071291, 0.2533471031358, 0.430727299295457,
- 0.622925723210088, 0.841621233572914, 1.11077161663679,
- 1.50108594604402]),
- 16: np.array([-np.inf, -1.53412054435255, -1.15034938037601,
- -0.887146559018876, -0.674489750196082,
- -0.488776411114669, -0.318639363964375,
- -0.157310684610171, 0, 0.157310684610171,
- 0.318639363964375, 0.488776411114669, 0.674489750196082,
- 0.887146559018876, 1.15034938037601, 1.53412054435255]),
- 17: np.array([-np.inf, -1.5647264713618, -1.18683143275582,
- -0.928899491647271, -0.721522283982343,
- -0.541395085129088, -0.377391943828554,
- -0.223007830940367, -0.0737912738082727,
- 0.0737912738082727, 0.223007830940367, 0.377391943828554,
- 0.541395085129088, 0.721522283982343, 0.928899491647271,
- 1.18683143275582, 1.5647264713618]),
- 18: np.array([-np.inf, -1.59321881802305, -1.22064034884735,
- -0.967421566101701, -0.764709673786387,
- -0.589455797849779, -0.430727299295457,
- -0.282216147062508, -0.139710298881862, 0,
- 0.139710298881862, 0.282216147062508, 0.430727299295457,
- 0.589455797849779, 0.764709673786387, 0.967421566101701,
- 1.22064034884735, 1.59321881802305]),
- 19: np.array([-np.inf, -1.61985625863827, -1.25211952026522,
- -1.00314796766253, -0.8045963803603, -0.633640000779701,
- -0.47950565333095, -0.336038140371823, -0.199201324789267,
- -0.0660118123758407, 0.0660118123758406,
- 0.199201324789267, 0.336038140371823, 0.47950565333095,
- 0.633640000779701, 0.8045963803603, 1.00314796766253,
- 1.25211952026522, 1.61985625863827]),
- 20: np.array([-np.inf, -1.64485362695147, -1.2815515655446,
- -1.03643338949379, -0.841621233572914, -0.674489750196082,
- -0.524400512708041, -0.385320466407568, -0.2533471031358,
- -0.125661346855074, 0, 0.125661346855074, 0.2533471031358,
- 0.385320466407568, 0.524400512708041, 0.674489750196082,
- 0.841621233572914, 1.03643338949379, 1.2815515655446,
- 1.64485362695147]),
- }
+ get_cuts_for_num(3)
+
+ Args:
+ a_size (int): Number of sections to cut into 
+ (Must be between 2 and 26)
+
+ Returns:
+ numpy.array: Numpy array of the threshold of the cuts
+ """ 
+
+ # Only limit too 26 sections as there are only 26 letters in the alphabet
+ if a_size > 26 or a_size < 2:
+ raise ValueError("'a_size must be between 2 and 26'") 
+
+ break_points = [-np.inf]
 
- return options[a_size]
+ for i in range(1, a_size):
+ break_points.append(scipy.stats.norm.ppf(i/a_size))
+
+ return np.array(break_points)

saxpy/hotsax.py

@@ -54,7 +54,7 @@ def find_best_discord_hotsax(series, win_size, global_registry, sax_data, magic_
 
  distance_calls = 0
 
- visit_array = np.zeros(len(series), dtype=np.int)
+ visit_array = np.zeros(len(series), dtype=np.int64)
 
  """[4.0] and we are off iterating over the magic array entries"""
  for entry in magic_array:

saxpy/strfunc.py

@@ -3,7 +3,7 @@
 
 def idx2letter(idx):
  """Convert a numerical index to a char."""
- if 0 <= idx < 20:
+ if 0 <= idx < 26:
  return chr(97 + idx)
  else:
  raise ValueError('A wrong idx value supplied.')

tests/test_cuts.py

@@ -4,5 +4,5 @@
 
 def test_sizing():
  """Test alphabet sizes."""
- for s in range(2, 20):
+ for s in range(2, 26):
  assert len(alphabet.cuts_for_asize(s)) == s

tests/test_str.py

@@ -8,9 +8,10 @@ def test_sizing():
  assert 'a' == idx2letter(0)
  assert 'h' == idx2letter(7)
  assert 't' == idx2letter(19)
+ assert 'z' == idx2letter(25)
 
  with pytest.raises(ValueError, match=r'.* idx'):
  idx2letter(-1)
 
  with pytest.raises(ValueError, match=r'.* idx .*'):
- idx2letter(20)
+ idx2letter(26)

tests/test_ts2string.py

@@ -6,18 +6,27 @@
 
 def test_stringing():
  """Test string conversion."""
- # 11: np.array([-np.inf, -1.33517773611894, -0.908457868537385,
- # -0.604585346583237, -0.348755695517045,
- # -0.114185294321428, 0.114185294321428, 0.348755695517045,
- # 0.604585346583237, 0.908457868537385, 1.33517773611894]),
- ab = sax.ts_to_string(np.array([-1.33517773611895, -1.33517773611894]),
+ # 11: np.array([-inf, -1.33517774, -0.90845787, -0.60458535,
+ #  -0.3487557, -0.11418529, 0.11418529, 0.3487557,
+ #  0.60458535, 0.90845787, 1.33517774]),
+
+ ab = sax.ts_to_string(np.array([-1.33517775 , -1.33517773]),
  alphabet.cuts_for_asize(11))
  assert 'ab' == ab
 
- kj = sax.ts_to_string(np.array([1.33517773611895, 1.33517773611894]),
+ kj = sax.ts_to_string(np.array([1.33517775, 1.33517773]),
  alphabet.cuts_for_asize(11))
  assert 'kj' == kj
 
+ # Test to handel cuts of size 26
+ print(alphabet.cuts_for_asize(26))
+
+ yz = sax.ts_to_string(np.array([1.76882503, 1.76882505]),
+ alphabet.cuts_for_asize(26))
+
+ assert 'yz' == yz
+
+
 
 def test_mindist():
  """Test MINDIST."""