Develop

.github/workflows/run-notebooks.yaml

@@ -8,6 +8,9 @@ on:
     branches: [ main, develop ]
   pull_request:
     branches: [ main, develop ]
+  schedule:
+    # run every month on 6th day
+    - cron: '0 0 6 * *'
 
 jobs:
   jupyter-notebooks:

.pre-commit-config.yaml

@@ -15,11 +15,11 @@ repos:
         name: Check file encoding
         entry: bash -c 'for file in "$@"; do file --mime-encoding $file | grep -q "ascii\|binary"; if [ $? != 0 ]; then echo $file; exit 1; fi; done' --
         types: [text]
-    # -   id: flake8
-    #     name: Check Python format
-    #     entry: flake8 --count --show-source --statistics
-    #     language: system
-    #     types: [python]
+    -   id: flake8
+        name: Check Python format
+        entry: flake8 --count --show-source --statistics
+        language: system
+        types: [python]
     -   id: unittest
         name: Run Python unittests
         language: system

prml/bayesnet/discrete.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from prml.bayesnet.probability_function import ProbabilityFunction
 from prml.bayesnet.random_variable import RandomVariable
 
@@ -35,7 +36,7 @@ def __init__(self, n_class:int):
         self.is_observed = False
 
     def __repr__(self):
-        string = f"DiscreteVariable("
+        string = "DiscreteVariable("
         if self.is_observed:
             string += f"observed={self.proba})"
         else:
@@ -201,7 +202,8 @@ def send_message(self, proprange, exclude=None):
             if random_variable is not exclude:
                 self.send_message_to(random_variable, proprange)
 
-        if proprange == 0: return
+        if proprange == 0:
+            return
 
         for random_variable in self.condition:
             if random_variable is not exclude:

prml/clustering/__init__.py

@@ -1,4 +1,4 @@
-from .k_means import KMeans
+from prml.clustering.k_means import KMeans
 
 
 __all__ = [

prml/clustering/k_means.py

@@ -7,13 +7,13 @@ class KMeans(object):
     def __init__(self, n_clusters):
         self.n_clusters = n_clusters
 
-    def fit(self, X, iter_max=100):
+    def fit(self, x, iter_max=100):
         """
         perform k-means algorithm
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
         iter_max : int
             maximum number of iterations
@@ -23,31 +23,31 @@ def fit(self, X, iter_max=100):
         centers : (n_clusters, n_features) ndarray
             center of each cluster
         """
-        I = np.eye(self.n_clusters)
-        centers = X[np.random.choice(len(X), self.n_clusters, replace=False)]
+        eye = np.eye(self.n_clusters)
+        centers = x[np.random.choice(len(x), self.n_clusters, replace=False)]
         for _ in range(iter_max):
             prev_centers = np.copy(centers)
-            D = cdist(X, centers)
+            D = cdist(x, centers)
             cluster_index = np.argmin(D, axis=1)
-            cluster_index = I[cluster_index]
-            centers = np.sum(X[:, None, :] * cluster_index[:, :, None], axis=0) / np.sum(cluster_index, axis=0)[:, None]
+            cluster_index = eye[cluster_index]
+            centers = np.sum(x[:, None, :] * cluster_index[:, :, None], axis=0) / np.sum(cluster_index, axis=0)[:, None]
             if np.allclose(prev_centers, centers):
                 break
         self.centers = centers
 
-    def predict(self, X):
+    def predict(self, x):
         """
         calculate closest cluster center index
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
 
         Returns
         -------
         index : (sample_size,) ndarray
             indicates which cluster they belong
         """
-        D = cdist(X, self.centers)
+        D = cdist(x, self.centers)
         return np.argmin(D, axis=1)

prml/dimreduction/autoencoder.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from prml import nn
 
 

prml/dimreduction/bayesian_pca.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from prml.dimreduction.pca import PCA
 
 
@@ -26,7 +27,7 @@ def fit(self, X, iter_max=100, initial="random"):
         """
         initial_list = ["random", "eigen"]
         self.mean = np.mean(X, axis=0)
-        self.I = np.eye(self.n_components)
+        self.eye = np.eye(self.n_components)
         if initial not in initial_list:
             print("availabel initializations are {}".format(initial_list))
         if initial == "random":

prml/dimreduction/pca.py

@@ -15,14 +15,14 @@ def __init__(self, n_components):
         assert isinstance(n_components, int)
         self.n_components = n_components
 
-    def fit(self, X, method="eigen", iter_max=100):
-        """
-        maximum likelihood estimate of pca parameters
+    def fit(self, x, method="eigen", iter_max=100):
+        r"""Maximum likelihood estimate of pca parameters.
+
         x ~ \int_z N(x|Wz+mu,sigma^2)N(z|0,I)dz
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
         method : str
             method to estimate the parameters
@@ -46,111 +46,111 @@ def fit(self, X, method="eigen", iter_max=100):
         method_list = ["eigen", "em"]
         if method not in method_list:
             print("availabel methods are {}".format(method_list))
-        self.mean = np.mean(X, axis=0)
-        getattr(self, method)(X - self.mean, iter_max)
+        self.mean = np.mean(x, axis=0)
+        getattr(self, method)(x - self.mean, iter_max)
 
-    def eigen(self, X, *arg):
-        sample_size, n_features = X.shape
+    def eigen(self, x, *arg):
+        sample_size, n_features = x.shape
         if sample_size >= n_features:
-            cov = np.cov(X, rowvar=False)
+            cov = np.cov(x, rowvar=False)
             values, vectors = np.linalg.eigh(cov)
             index = n_features - self.n_components
         else:
-            cov = np.cov(X)
+            cov = np.cov(x)
             values, vectors = np.linalg.eigh(cov)
-            vectors = (X.T @ vectors) / np.sqrt(sample_size * values)
+            vectors = (x.T @ vectors) / np.sqrt(sample_size * values)
             index = sample_size - self.n_components
-        self.I = np.eye(self.n_components)
+        self.eye = np.eye(self.n_components)
         if index == 0:
             self.var = 0
         else:
             self.var = np.mean(values[:index])
 
-        self.W = vectors[:, index:].dot(np.sqrt(np.diag(values[index:]) - self.var * self.I))
-        self.__M = self.W.T @ self.W + self.var * self.I
+        self.W = vectors[:, index:].dot(np.sqrt(np.diag(values[index:]) - self.var * self.eye))
+        self.__M = self.W.T @ self.W + self.var * self.eye
         self.C = self.W @ self.W.T + self.var * np.eye(n_features)
         if index == 0:
             self.Cinv = np.linalg.inv(self.C)
         else:
             self.Cinv = np.eye(n_features) / np.sqrt(self.var) - self.W @ np.linalg.inv(self.__M) @ self.W.T / self.var
 
-    def em(self, X, iter_max):
-        self.I = np.eye(self.n_components)
-        self.W = np.eye(np.size(X, 1), self.n_components)
+    def em(self, x, iter_max):
+        self.eye = np.eye(self.n_components)
+        self.W = np.eye(np.size(x, 1), self.n_components)
         self.var = 1.
         for i in range(iter_max):
             W = np.copy(self.W)
-            stats = self._expectation(X)
-            self._maximization(X, *stats)
+            stats = self._expectation(x)
+            self._maximization(x, *stats)
             if np.allclose(W, self.W):
                 break
-        self.C = self.W @ self.W.T + self.var * np.eye(np.size(X, 1))
+        self.C = self.W @ self.W.T + self.var * np.eye(np.size(x, 1))
         self.Cinv = np.linalg.inv(self.C)
 
-    def _expectation(self, X):
-        self.__M = self.W.T @ self.W + self.var * self.I
+    def _expectation(self, x):
+        self.__M = self.W.T @ self.W + self.var * self.eye
         Minv = np.linalg.inv(self.__M)
-        Ez = X @ self.W @ Minv
+        Ez = x @ self.W @ Minv
         Ezz = self.var * Minv + Ez[:, :, None] * Ez[:, None, :]
         return Ez, Ezz
 
-    def _maximization(self, X, Ez, Ezz):
-        self.W = X.T @ Ez @ np.linalg.inv(np.sum(Ezz, axis=0))
+    def _maximization(self, x, Ez, Ezz):
+        self.W = x.T @ Ez @ np.linalg.inv(np.sum(Ezz, axis=0))
         self.var = np.mean(
-            np.mean(X ** 2, axis=1)
-            - 2 * np.mean(Ez @ self.W.T * X, axis=1)
-            + np.trace((Ezz @ self.W.T @ self.W).T) / np.size(X, 1))
+            np.mean(x ** 2, axis=1)
+            - 2 * np.mean(Ez @ self.W.T * x, axis=1)
+            + np.trace((Ezz @ self.W.T @ self.W).T) / np.size(x, 1))
 
-    def transform(self, X):
+    def transform(self, x):
         """
         project input data into latent space
-        p(Z|X) = N(Z|(X-mu)WMinv, sigma^-2M)
+        p(Z|x) = N(Z|(x-mu)WMinv, sigma^-2M)
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
 
         Returns
         -------
         Z : (sample_size, n_components) ndarray
             projected input data
         """
-        return np.linalg.solve(self.__M, ((X - self.mean) @ self.W).T).T
+        return np.linalg.solve(self.__M, ((x - self.mean) @ self.W).T).T
 
-    def fit_transform(self, X, method="eigen"):
+    def fit_transform(self, x, method="eigen"):
         """
         perform pca and whiten the input data
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
 
         Returns
         -------
         Z : (sample_size, n_components) ndarray
             projected input data
         """
-        self.fit(X, method)
-        return self.transform(X)
+        self.fit(x, method)
+        return self.transform(x)
 
-    def proba(self, X):
+    def proba(self, x):
         """
         the marginal distribution of the observed variable
 
         Parameters
         ----------
-        X : (sample_size, n_features) ndarray
+        x : (sample_size, n_features) ndarray
             input data
 
         Returns
         -------
         p : (sample_size,) ndarray
             value of the marginal distribution
         """
-        d = X - self.mean
+        d = x - self.mean
         return (
             np.exp(-0.5 * np.sum(d @ self.Cinv * d, axis=-1))
             / np.sqrt(np.linalg.det(self.C))
-            / np.power(2 * np.pi, 0.5 * np.size(X, 1)))
+            / np.power(2 * np.pi, 0.5 * np.size(x, 1)))

prml/kernel/__init__.py

@@ -1,8 +1,7 @@
-from prml.kernel.polynomial import PolynomialKernel
-from prml.kernel.rbf import RBF
-
 from prml.kernel.gaussian_process_classifier import GaussianProcessClassifier
 from prml.kernel.gaussian_process_regressor import GaussianProcessRegressor
+from prml.kernel.polynomial import PolynomialKernel
+from prml.kernel.rbf import RBF
 from prml.kernel.relevance_vector_classifier import RelevanceVectorClassifier
 from prml.kernel.relevance_vector_regressor import RelevanceVectorRegressor
 from prml.kernel.support_vector_classifier import SupportVectorClassifier

prml/kernel/gaussian_process_classifier.py

@@ -20,18 +20,18 @@ def __init__(self, kernel, noise_level=1e-4):
     def _sigmoid(self, a):
         return np.tanh(a * 0.5) * 0.5 + 0.5
 
-    def fit(self, X, t):
-        if X.ndim == 1:
-            X = X[:, None]
-        self.X = X
+    def fit(self, x, t):
+        if x.ndim == 1:
+            x = x[:, None]
+        self.x = x
         self.t = t
-        Gram = self.kernel(X, X)
+        Gram = self.kernel(x, x)
         self.covariance = Gram + np.eye(len(Gram)) * self.noise_level
         self.precision = np.linalg.inv(self.covariance)
 
-    def predict(self, X):
-        if X.ndim == 1:
-            X = X[:, None]
-        K = self.kernel(X, self.X)
+    def predict(self, x):
+        if x.ndim == 1:
+            x = x[:, None]
+        K = self.kernel(x, self.x)
         a_mean = K @ self.precision @ self.t
         return self._sigmoid(a_mean)

prml/kernel/gaussian_process_regressor.py

@@ -49,18 +49,18 @@ def fit(self, X, t, iter_max=0, learning_rate=0.1):
         log_likelihood_list = [-np.inf]
         self.X = X
         self.t = t
-        I = np.eye(len(X))
-        Gram = self.kernel(X, X)
-        self.covariance = Gram + I / self.beta
+        eye = np.eye(len(X))
+        gram = self.kernel(X, X)
+        self.covariance = gram + eye / self.beta
         self.precision = np.linalg.inv(self.covariance)
         for i in range(iter_max):
             gradients = self.kernel.derivatives(X, X)
             updates = np.array(
                 [-np.trace(self.precision.dot(grad)) + t.dot(self.precision.dot(grad).dot(self.precision).dot(t)) for grad in gradients])
             for j in range(iter_max):
                 self.kernel.update_parameters(learning_rate * updates)
-                Gram = self.kernel(X, X)
-                self.covariance = Gram + I / self.beta
+                gram = self.kernel(X, X)
+                self.covariance = gram + eye / self.beta
                 self.precision = np.linalg.inv(self.covariance)
                 log_like = self.log_likelihood()
                 if log_like > log_likelihood_list[-1]:

prml/kernel/polynomial.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from prml.kernel.kernel import Kernel
 
 

prml/kernel/rbf.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from prml.kernel.kernel import Kernel