Merge pull request scikit-learn#3230 from bwignall/quickfix-gaussian

CLN: Capitalize "Gaussian" in example docstrings

Author: agramfort

examples/cluster/plot_kmeans_stability_low_dim_dense.py

@@ -18,7 +18,7 @@
 a bad convergence (local optimum) with estimated centers between stucked
 between ground truth clusters.
 
-The dataset used for evaluation is a 2D grid of isotropic gaussian
+The dataset used for evaluation is a 2D grid of isotropic Gaussian
 clusters widely spaced.
 """
 print(__doc__)

examples/covariance/plot_lw_vs_oas.py

@@ -10,10 +10,10 @@
 
 Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage
 parameter, the OAS coefficient, whose convergence is significantly
-better under the assumption that the data are gaussian.
+better under the assumption that the data are Gaussian.
 
 This example, inspired from Chen's publication [1], shows a comparison
-of the estimated MSE of the LW and OAS methods, using gaussian
+of the estimated MSE of the LW and OAS methods, using Gaussian
 distributed data.
 
 [1] "Shrinkage Algorithms for MMSE Covariance Estimation"

examples/covariance/plot_mahalanobis_distances.py

@@ -10,7 +10,7 @@
 :math:`x_i` to the mode of the distribution can be computed using its
 Mahalanobis distance: :math:`d_{(\mu,\Sigma)}(x_i)^2 = (x_i -
 \mu)'\Sigma^{-1}(x_i - \mu)` where :math:`\mu` and :math:`\Sigma` are
-the location and the covariance of the underlying gaussian
+the location and the covariance of the underlying Gaussian
 distribution.
 
 In practice, :math:`\mu` and :math:`\Sigma` are replaced by some

examples/decomposition/plot_image_denoising.py

@@ -15,7 +15,7 @@
 
 A common practice for evaluating the results of image denoising is by looking
 at the difference between the reconstruction and the original image. If the
-reconstruction is perfect this will look like gaussian noise.
+reconstruction is perfect this will look like Gaussian noise.
 
 It can be seen from the plots that the results of :ref:`omp` with two
 non-zero coefficients is a bit less biased than when keeping only one

examples/decomposition/plot_sparse_coding.py

@@ -6,7 +6,7 @@
 Transform a signal as a sparse combination of Ricker wavelets. This example
 visually compares different sparse coding methods using the
 :class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known
-as mexican hat or the second derivative of a gaussian) is not a particularly
+as mexican hat or the second derivative of a Gaussian) is not a particularly
 good kernel to represent piecewise constant signals like this one. It can
 therefore be seen how much adding different widths of atoms matters and it
 therefore motivates learning the dictionary to best fit your type of signals.

examples/linear_model/plot_ard.py

@@ -25,12 +25,12 @@
 from sklearn.linear_model import ARDRegression, LinearRegression
 
 ###############################################################################
-# Generating simulated data with Gaussian weigthts
+# Generating simulated data with Gaussian weights
 
 # Parameters of the example
 np.random.seed(0)
 n_samples, n_features = 100, 100
-# Create gaussian data
+# Create Gaussian data
 X = np.random.randn(n_samples, n_features)
 # Create weigts with a precision lambda_ of 4.
 lambda_ = 4.

examples/linear_model/plot_bayesian_ridge.py

@@ -28,7 +28,7 @@
 # Generating simulated data with Gaussian weigthts
 np.random.seed(0)
 n_samples, n_features = 100, 100
-X = np.random.randn(n_samples, n_features)  # Create gaussian data
+X = np.random.randn(n_samples, n_features)  # Create Gaussian data
 # Create weigts with a precision lambda_ of 4.
 lambda_ = 4.
 w = np.zeros(n_features)

examples/linear_model/plot_logistic.py

@@ -24,7 +24,7 @@
 from sklearn import linear_model
 
 # this is our test set, it's just a straight line with some
-# gaussian noise
+# Gaussian noise
 xmin, xmax = -5, 5
 n_samples = 100
 np.random.seed(0)

examples/mixture/plot_gmm.py

@@ -3,7 +3,7 @@
 Gaussian Mixture Model Ellipsoids
 =================================
 
-Plot the confidence ellipsoids of a mixture of two gaussians with EM
+Plot the confidence ellipsoids of a mixture of two Gaussians with EM
 and variational dirichlet process.
 
 Both models have access to five components with which to fit the
@@ -38,11 +38,11 @@
 X = np.r_[np.dot(np.random.randn(n_samples, 2), C),
           .7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]
 
-# Fit a mixture of gaussians with EM using five components
+# Fit a mixture of Gaussians with EM using five components
 gmm = mixture.GMM(n_components=5, covariance_type='full')
 gmm.fit(X)
 
-# Fit a dirichlet process mixture of gaussians using five components
+# Fit a dirichlet process mixture of Gaussians using five components
 dpgmm = mixture.DPGMM(n_components=5, covariance_type='full')
 dpgmm.fit(X)
 

examples/mixture/plot_gmm_pdf.py

@@ -3,8 +3,8 @@
 Density Estimation for a mixture of Gaussians
 =============================================
 
-Plot the density estimation of a mixture of two gaussians. Data is
-generated from two gaussians with different centers and covariance
+Plot the density estimation of a mixture of two Gaussians. Data is
+generated from two Gaussians with different centers and covariance
 matrices.
 """
 
@@ -21,7 +21,7 @@
 # generate spherical data centered on (20, 20)
 shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20])
 
-# generate zero centered stretched gaussian data
+# generate zero centered stretched Gaussian data
 C = np.array([[0., -0.7], [3.5, .7]])
 stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C)