Merge pull request #85 from MatthewSZhang/codespell

MNT add codespell

Author: MatthewSZhang

.github/workflows/static.yml

@@ -30,3 +30,6 @@ jobs:
       - name: Type check
         run: |
           pixi run type
+      - name: Spell check
+        run: |
+          pixi run spell

doc/multioutput.rst

@@ -12,7 +12,7 @@ MIMO (Multi-Input Multi-Output) data. For classification, it can be used for
 multilabel data. Actually, for multiclass classification, which has one output with
 multiple categories, multioutput feature selection can also be useful. The multiclass
 classification can be converted to multilabel classification by one-hot encoding
-target ``y``. The cannonical correaltion coefficient between the features ``X`` and the
+target ``y``. The canonical correaltion coefficient between the features ``X`` and the
 one-hot encoded target ``y`` has equivalent relationship with Fisher's criterion in
 LDA (Linear Discriminant Analysis) [1]_. Applying :class:`FastCan` to the converted
 multioutput data may result in better accuracy in the following classification task

doc/narx.rst

@@ -82,7 +82,7 @@ It should also be noted the different types of predictions in model training.
 ARX and OE model
 ----------------
 
-To better understant the two types of training, it is helpful to know two linear time series model structures,
+To better understand the two types of training, it is helpful to know two linear time series model structures,
 i.e., `ARX (AutoRegressive eXogenous) model <https://www.mathworks.com/help/ident/ref/arx.html>`_ and
 `OE (output error) model <https://www.mathworks.com/help/ident/ref/oe.html>`_.
 

doc/ols_and_omp.rst

@@ -12,7 +12,7 @@ The detailed difference between OLS and OMP can be found in [3]_.
 Here, let's briefly compare the three methods.
 
 
-Assume we have a feature matrix :math:`X_s \in \mathbb{R}^{N\times t}`, which constains
+Assume we have a feature matrix :math:`X_s \in \mathbb{R}^{N\times t}`, which contains
 :math:`t` selected features, and a target vector :math:`y \in \mathbb{R}^{N\times 1}`.
 Then the residual :math:`r \in \mathbb{R}^{N\times 1}` of the least-squares can be
 found by

doc/pruning.rst

@@ -22,9 +22,9 @@ should be selected, as any additional samples can be represented by linear combi
 Therefore, the number to select has to be set to small.
 
 To solve this problem, we use :func:`minibatch` to loose the redundancy check of :class:`FastCan`.
-The original :class:`FastCan` checks the redunancy within :math:`X_s \in \mathbb{R}^{n\times t}`, 
+The original :class:`FastCan` checks the redundancy within :math:`X_s \in \mathbb{R}^{n\times t}`, 
 which contains :math:`t` selected samples and n features,
-and the redunancy within :math:`Y \in \mathbb{R}^{n\times m}`, which contains :math:`m` atoms :math:`y_i`.
+and the redundancy within :math:`Y \in \mathbb{R}^{n\times m}`, which contains :math:`m` atoms :math:`y_i`.
 :func:`minibatch` ranks samples with multiple correlation coefficients between :math:`X_b \in \mathbb{R}^{n\times b}` and :math:`y_i`,
 where :math:`b` is batch size and :math:`b <= t`, instead of canonical correlation coefficients between :math:`X_s` and :math:`Y`,
 which is used in :class:`FastCan`.

examples/plot_fisher.py

@@ -5,7 +5,7 @@
 
 .. currentmodule:: fastcan
 
-In this examples, we will demonstrate the cannonical correaltion coefficient
+In this examples, we will demonstrate the canonical correaltion coefficient
 between the features ``X`` and the one-hot encoded target ``y`` has equivalent
 relationship with Fisher's criterion in LDA (Linear Discriminant Analysis).
 """

examples/plot_intuitive.py

@@ -22,10 +22,10 @@
 # the predicted target by a linear regression model) and the target to describe its
 # usefulness, the results are shown in the following figure. It can be seen that
 # Feature 2 is the most useful and Feature 8 is the second. However, does that mean
-# that the total usefullness of Feature 2 + Feature 8 is the sum of their R-squared
+# that the total usefulness of Feature 2 + Feature 8 is the sum of their R-squared
 # scores? Probably not, because there may be redundancy between Feature 2 and Feature 8.
 # Actually, what we want is a kind of usefulness score which has the **superposition**
-# property, so that the usefullness of each feature can be added together without
+# property, so that the usefulness of each feature can be added together without
 # redundancy.
 
 import matplotlib.pyplot as plt
@@ -125,7 +125,7 @@ def plot_bars(ids, r2_left, r2_selected):
 # Select the third feature
 # ------------------------
 # Again, let's compute the R-squared between Feature 2 + Feature 8 + Feature i and
-# the target, and the additonal R-squared contributed by the rest of the features is
+# the target, and the additional R-squared contributed by the rest of the features is
 # shown in following figure. It can be found that after selecting Features 2 and 8, the
 # rest of the features can provide a very limited contribution.
 
@@ -145,8 +145,8 @@ def plot_bars(ids, r2_left, r2_selected):
 # at the RHS of the dashed lines. The fast computational speed is achieved by
 # orthogonalization, which removes the redundancy between the features. We use the
 # orthogonalization first to makes the rest of features orthogonal to the selected
-# features and then compute their additonal R-squared values. ``eta-cosine`` uses
-# the samilar idea, but has an additonal preprocessing step to compress the features
+# features and then compute their additional R-squared values. ``eta-cosine`` uses
+# the similar idea, but has an additional preprocessing step to compress the features
 # :math:`X \in \mathbb{R}^{N\times n}` and the target
 # :math:`X \in \mathbb{R}^{N\times n}` to :math:`X_c \in \mathbb{R}^{(m+n)\times n}`
 # and :math:`Y_c \in \mathbb{R}^{(m+n)\times m}`.

examples/plot_pruning.py

@@ -81,7 +81,7 @@ def _fastcan_pruning(
 # %%
 # Compare pruning methods
 # -----------------------
-# 100 samples are seleced from 150 original data with ``Random`` pruning and
+# 100 samples are selected from 150 original data with ``Random`` pruning and
 # ``FastCan`` pruning. The results show that ``FastCan`` pruning gives a higher
 # mean value of R-squared and a lower standard deviation.
 

examples/plot_redundancy.py

@@ -9,7 +9,7 @@
 datasets, which contain redundant features.
 Here four types of features should be distinguished:
 
-* Unuseful features: the features do not contribute to the target
+* Useless features: the features do not contribute to the target
 * Dependent informative features: the features contribute to the target and form
   the redundant features
 * Redundant features: the features are constructed by linear transformation of

examples/plot_speed.py

@@ -147,7 +147,7 @@ def baseline(X, y, t):
 r_eta = FastCan(n_features_to_select, eta=True, verbose=0).fit(X, y).indices_
 r_base, _ = baseline(X, y, n_features_to_select)
 
-print("The indices of the seleted features:", end="\n")
+print("The indices of the selected features:", end="\n")
 print(f"h-correlation: {r_h}")
 print(f"eta-cosine: {r_eta}")
 print(f"Baseline: {r_base}")