[patch:lib] Rename normalize function to center

README.md

@@ -59,7 +59,7 @@ Sequentia offers the use of **multivariate observation sequences with differing
 
 ### Preprocessing methods
 
-- [x] Normalization
+- [x] Centering
 - [x] Downsampling (decimation and averaging)
 - [x] Filtering (mean and median)
 - [x] Discrete Fourier Transform

docs/_includes/examples/preprocessing/normalize.py → docs/_includes/examples/preprocessing/center.py

@@ -1,8 +1,8 @@
 import numpy as np
-from sequentia.preprocessing import normalize
+from sequentia.preprocessing import center
 
 # Create some sample data
 X = [np.random.random((10 * i, 3)) for i in range(1, 4)]
 
-# Normalize the data
-X = normalize(X)
+# Center the data
+X = center(X)

docs/_includes/examples/preprocessing/preprocess.py

@@ -6,7 +6,7 @@
 
 # Create the Preprocess object
 pre = Preprocess()
-pre.normalize()
+pre.center()
 pre.filtrate(n=5, method='median')
 pre.downsample(n=5, method='decimate')
 pre.fft()

docs/index.rst

@@ -45,7 +45,7 @@ Sequentia offers some appropriate classification algorithms for these kinds of t
    :maxdepth: 1
    :caption: Preprocessing Methods
 
-   sections/preprocessing/normalize.rst
+   sections/preprocessing/center.rst
    sections/preprocessing/downsample.rst
    sections/preprocessing/filtrate.rst
    sections/preprocessing/fft.rst

docs/sections/preprocessing/normalize.rst → docs/sections/preprocessing/center.rst

@@ -1,9 +1,9 @@
-.. _normalize:
+.. _center:
 
-Normalization (``normalize``)
+Centering (``center``)
 =============================
 
-Normalizing centers an observation sequence about the mean of its observations – that is, given:
+Centers an observation sequence about the mean of its observations – that is, given:
 
 .. math::
 
@@ -22,17 +22,17 @@ Where :math:`\overline{o_d}` represents the mean of the :math:`d^\text{th}` feat
 
 We subtract :math:`\boldsymbol{\mu}` from each observation, or row in :math:`O`. This centers the observations.
 
-For further information, please see the `preprocessing tutorial notebook <https://nbviewer.jupyter.org/github/eonu/sequentia/blob/master/notebooks/2%20-%20Preprocessing%20%28Tutorial%29.ipynb#Normalization-(normalize)>`_.
+For further information, please see the `preprocessing tutorial notebook <https://nbviewer.jupyter.org/github/eonu/sequentia/blob/master/notebooks/2%20-%20Preprocessing%20%28Tutorial%29.ipynb#Centering-(center)>`_.
 
 Example
 -------
 
-.. literalinclude:: ../../_includes/examples/preprocessing/normalize.py
+.. literalinclude:: ../../_includes/examples/preprocessing/center.py
     :language: python
     :linenos:
 
 API reference
 -------------
 
 .. automodule:: sequentia.preprocessing
-.. autofunction:: normalize
+.. autofunction:: center

lib/sequentia/preprocessing/__init__.py

@@ -1,5 +1,5 @@
 from .methods import (
-    downsample, normalize, fft, filtrate,
-    _downsample, _normalize, _fft, _filtrate
+    downsample, center, fft, filtrate,
+    _downsample, _center, _fft, _filtrate
 )
 from .preprocess import Preprocess

lib/sequentia/preprocessing/methods.py

@@ -2,8 +2,8 @@
 import numpy as np
 from ..internals import _Validator
 
-def normalize(X):
-    """Normalizes an observation sequence (or multiple sequences) by centering observations around the mean.
+def center(X):
+    """Centers an observation sequence (or multiple sequences) by centering observations around the mean.
 
     Parameters
     ----------
@@ -12,14 +12,14 @@ def normalize(X):
 
     Returns
     -------
-    normalized: numpy.ndarray or List[numpy.ndarray]
-        The normalized input observation sequence(s).
+    centered: numpy.ndarray or List[numpy.ndarray]
+        The centered input observation sequence(s).
     """
     val = _Validator()
     val.observation_sequences(X, allow_single=True)
-    return _normalize(X)
+    return _center(X)
 
-def _normalize(X):
+def _center(X):
     def transform(x):
         return x - x.mean(axis=0)
 

lib/sequentia/preprocessing/preprocess.py

@@ -1,5 +1,5 @@
 import numpy as np
-from .methods import _normalize, _downsample, _fft, _filtrate
+from .methods import _center, _downsample, _fft, _filtrate
 from ..internals import _Validator
 
 class Preprocess:
@@ -9,9 +9,9 @@ def __init__(self):
         self._transforms = []
         self._val = _Validator()
 
-    def normalize(self):
-        """Normalizes an observation sequence (or multiple sequences) by centering observations around the mean."""
-        self._transforms.append((_normalize, {}))
+    def center(self):
+        """Centers an observation sequence (or multiple sequences) by centering observations around the mean."""
+        self._transforms.append((_center, {}))
 
     def downsample(self, n, method='decimate'):
         """Downsamples an observation sequence (or multiple sequences) by either:
@@ -93,8 +93,8 @@ def summary(self):
 
         for i, (transform, kwargs) in enumerate(self._transforms):
             idx = i + 1
-            if transform == _normalize:
-                steps.append(('{}. Normalization'.format(idx), None))
+            if transform == _center:
+                steps.append(('{}. Centering'.format(idx), None))
             elif transform == _downsample:
                 header = 'Decimating' if kwargs['method'] == 'decimate' else 'Averaging'
                 steps.append((

lib/test/lib/preprocessing/test_methods.py

@@ -1,6 +1,6 @@
 import pytest
 import numpy as np
-from sequentia.preprocessing import downsample, normalize, fft, filtrate
+from sequentia.preprocessing import downsample, center, fft, filtrate
 from ...support import assert_equal, assert_all_equal
 
 # Set seed for reproducible randomness
@@ -13,13 +13,13 @@
 X_odd = rng.random((7, 2))
 Xs = [i * rng.random((3 * i, 2)) for i in range(1, 4)]
 
-# =========== #
-# normalize() #
-# =========== #
+# ======== #
+# center() #
+# ======== #
 
-def test_normalize_single_even():
-    """Normalize a single even-length observation sequence"""
-    assert_equal(normalize(X_even), np.array([
+def test_center_single_even():
+    """Center a single even-length observation sequence"""
+    assert_equal(center(X_even), np.array([
         [-0.07922094, 0.09684335 ],
         [-0.02527107, -0.07346283],
         [-0.20437965, 0.0275481  ],
@@ -28,9 +28,9 @@ def test_normalize_single_even():
         [0.16369059 , -0.0894511 ]
     ]))
 
-def test_normalize_single_odd():
-    """Normalize a single odd-length observation sequence"""
-    assert_equal(normalize(X_odd), np.array([
+def test_center_single_odd():
+    """Center a single odd-length observation sequence"""
+    assert_equal(center(X_odd), np.array([
         [0.14006915 , 0.2206014  ],
         [-0.35693936, -0.61786594],
         [-0.40775702, 0.1276246  ],
@@ -40,9 +40,9 @@ def test_normalize_single_odd():
         [-0.30970099, -0.06507422]
     ]))
 
-def test_normalize_multiple():
-    """Normalize multiple observation sequences"""
-    assert_all_equal(normalize(Xs), [
+def test_center_multiple():
+    """Center multiple observation sequences"""
+    assert_all_equal(center(Xs), [
         np.array([
             [-0.16656579, 0.23348073 ],
             [0.21192925 , -0.29652624],

lib/test/lib/preprocessing/test_preprocess.py

@@ -2,8 +2,8 @@
 import numpy as np
 from sequentia.preprocessing import (
     Preprocess,
-    downsample, normalize, fft, filtrate,
-    _downsample, _normalize, _fft, _filtrate
+    downsample, center, fft, filtrate,
+    _downsample, _center, _fft, _filtrate
 )
 from ...support import assert_equal, assert_all_equal
 
@@ -16,9 +16,9 @@
 X = rng.random((7, 2))
 Xs = [i * rng.random((3 * i, 2)) for i in range(1, 4)]
 
-# Normalization preprocessor
+# Centering preprocessor
 norm = Preprocess()
-norm.normalize()
+norm.center()
 
 # Discrete Fourier Transform preprocessor
 fourier = Preprocess()
@@ -36,35 +36,35 @@
 
 # Combined preprocessor
 combined = Preprocess()
-combined.normalize()
+combined.center()
 combined.filtrate(**filt_kwargs)
 combined.downsample(**down_kwargs)
 combined.fft()
 
-# ====================== #
-# Preprocess.normalize() #
-# ====================== #
+# =================== #
+# Preprocess.center() #
+# =================== #
 
-def test_normalize_adds_transform():
-    """Applying a single normalization transformation"""
+def test_center_adds_transform():
+    """Applying a single centering transformation"""
     assert len(norm._transforms) == 1
-    assert norm._transforms[0] == (_normalize, {})
+    assert norm._transforms[0] == (_center, {})
 
-def test_normalize_single():
-    """Applying normalization to a single observation sequence"""
-    assert_equal(norm.transform(X), normalize(X))
+def test_center_single():
+    """Applying centering to a single observation sequence"""
+    assert_equal(norm.transform(X), center(X))
 
-def test_normalize_multiple():
-    """Applying normalization to multiple observation sequences"""
-    assert_all_equal(norm.transform(Xs), normalize(Xs))
+def test_center_multiple():
+    """Applying centering to multiple observation sequences"""
+    assert_all_equal(norm.transform(Xs), center(Xs))
 
-def test_normalize_summary(capsys):
-    """Summary of a normalization transformation"""
+def test_center_summary(capsys):
+    """Summary of a centering transformation"""
     norm.summary()
     assert capsys.readouterr().out == (
         'Preprocessing summary:\n'
         '======================\n'
-        '1. Normalization\n'
+        '1. Centering\n'
         '======================\n'
     )
 
@@ -159,7 +159,7 @@ def test_combined_adds_transforms():
     """Applying multiple filtering transformations"""
     assert len(combined._transforms) == 4
     assert combined._transforms == [
-        (_normalize, {}),
+        (_center, {}),
         (_filtrate, filt_kwargs),
         (_downsample, down_kwargs),
         (_fft, {})
@@ -168,7 +168,7 @@ def test_combined_adds_transforms():
 def test_combined_single():
     """Applying combined transformations to a single observation sequence"""
     X_pre = X
-    X_pre = normalize(X_pre)
+    X_pre = center(X_pre)
     X_pre = filtrate(X_pre, **filt_kwargs)
     X_pre = downsample(X_pre, **down_kwargs)
     X_pre = fft(X_pre)
@@ -177,7 +177,7 @@ def test_combined_single():
 def test_combined_multiple():
     """Applying combined transformations to multiple observation sequences"""
     Xs_pre = Xs
-    Xs_pre = normalize(Xs_pre)
+    Xs_pre = center(Xs_pre)
     Xs_pre = filtrate(Xs_pre, **filt_kwargs)
     Xs_pre = downsample(Xs_pre, **down_kwargs)
     Xs_pre = fft(Xs_pre)
@@ -189,7 +189,7 @@ def test_combined_summary(capsys):
     assert capsys.readouterr().out == (
         '          Preprocessing summary:          \n'
         '==========================================\n'
-        '1. Normalization\n'
+        '1. Centering\n'
         '------------------------------------------\n'
         '2. Filtering:\n'
         '   Median filter with window size (n=3)\n'