From bab1a74c1d521deaa15e8068608265a68ca1bb48 Mon Sep 17 00:00:00 2001
From: Mateusz <msluszniak@student.agh.edu.pl>
Date: Tue, 7 Nov 2023 20:52:32 +0100
Subject: [PATCH] Remove deprecated covariance

---
 lib/scholar/covariance.ex | 179 --------------------------------------
 1 file changed, 179 deletions(-)
 delete mode 100644 lib/scholar/covariance.ex

diff --git a/lib/scholar/covariance.ex b/lib/scholar/covariance.ex
deleted file mode 100644
index f1ffc37c..00000000
--- a/lib/scholar/covariance.ex
+++ /dev/null
@@ -1,179 +0,0 @@
-defmodule Scholar.Covariance do
-  @moduledoc ~S"""
-  Algorithms to estimate the covariance of features given a set of points.
-  """
-  import Nx.Defn
-
-  opts = [
-    center: [
-      type: :boolean,
-      default: true,
-      doc: """
-      If `true`, data will be centered before computation.
-      If `false`, data will not be centered before computation.
-      Useful when working with data whose mean is almost, but not exactly zero.
-      """
-    ],
-    biased: [
-      type: :boolean,
-      default: true,
-      doc: """
-      If `true`, the matrix will be computed using biased covariation. If `false`,
-      algorithm uses unbiased covariation.
-      """
-    ]
-  ]
-
-  @opts_schema NimbleOptions.new!(opts)
-
-  @deprecated "Use Nx.convariance/2 instead"
-  @doc """
-  Computes covariance matrix for sample inputs `x`.
-
-  The value on the position $Cov_{ij}$ in the $Cov$ matrix is calculated using the formula:
-
-  #{~S'''
-  $$ Cov(X\_i, X\_j) = \frac{\sum\_{k}\left(x\_k -
-  \bar{x}\right)\left(y\_k - \bar{y}\right)}{N - 1}
-  $$
-  Where:
-    * $X_i$ is a $i$th row of input
-
-    * $x_k$ is a $k$th value of $X_i$
-
-    * $y_k$ is a $k$th value of $X_j$
-
-    * $\bar{x}$ is the mean of $X_i$
-
-    * $\bar{y}$ is the mean of $X_j$
-
-    * $N$ is the number of samples
-
-  This is a non-biased version of covariance.
-  The biased version has $N$ in denominator instead of $N - 1$.
-  '''}
-
-  ## Options
-
-  #{NimbleOptions.docs(@opts_schema)}
-
-  ## Example
-
-      iex> Scholar.Covariance.covariance_matrix(Nx.tensor([[3, 6, 5], [26, 75, 3], [23, 4, 1]]))
-      #Nx.Tensor<
-        f32[3][3]
-        [
-          [104.22222137451172, 195.5555419921875, -13.333333015441895],
-          [195.5555419921875, 1089.5555419921875, 1.3333333730697632],
-          [-13.333333015441895, 1.3333333730697632, 2.6666667461395264]
-        ]
-      >
-
-      iex> Scholar.Covariance.covariance_matrix(Nx.tensor([[3, 6], [2, 3], [7, 9], [5, 3]]))
-      #Nx.Tensor<
-        f32[2][2]
-        [
-          [3.6875, 3.1875],
-          [3.1875, 6.1875]
-        ]
-      >
-
-      iex> Scholar.Covariance.covariance_matrix(Nx.tensor([[3, 6, 5], [26, 75, 3], [23, 4, 1]]),
-      ...>   biased: false
-      ...> )
-      #Nx.Tensor<
-        f32[3][3]
-        [
-          [156.3333282470703, 293.33331298828125, -20.0],
-          [293.33331298828125, 1634.333251953125, 2.0],
-          [-20.0, 2.0, 4.0]
-        ]
-      >
-  """
-  deftransform covariance_matrix(x, opts \\ []) do
-    covariance_matrix_n(x, NimbleOptions.validate!(opts, @opts_schema))
-  end
-
-  defnp covariance_matrix_n(x, opts) do
-    if Nx.rank(x) != 2 do
-      raise ArgumentError, "expected data to have rank equal 2, got: #{inspect(Nx.rank(x))}"
-    end
-
-    num_samples = Nx.axis_size(x, 0)
-    x = if opts[:center], do: x - Nx.mean(x, axes: [0]), else: x
-    matrix = Nx.dot(x, [0], x, [0])
-
-    if opts[:biased] do
-      matrix / num_samples
-    else
-      matrix / (num_samples - 1)
-    end
-  end
-
-  @deprecated "Use Scholar.Stats.correlation_matrix/2 instead"
-  @doc """
-  Computes correlation matrix for sample inputs `x`.
-
-  The value on the position $Corr_{ij}$ in the $Corr$ matrix is calculated using the formula:
-  #{~S'''
-  $$ Corr(X\_i, X\_j) = \frac{Cov(X\_i, X\_j)}{\sqrt{Cov(X\_i, X\_i)Cov(X\_j, X\_j)}} $$
-  Where:
-    * $X_i$ is a $i$th row of input
-
-    * $Cov(X\_i, X\_j)$ is covariance between features $X_i$ and $X_j$
-  '''}
-
-  ## Options
-
-  #{NimbleOptions.docs(@opts_schema)}
-
-  ## Example
-
-      iex> Scholar.Covariance.correlation_matrix(Nx.tensor([[3, 6, 5], [26, 75, 3], [23, 4, 1]]))
-      #Nx.Tensor<
-        f32[3][3]
-        [
-          [1.0, 0.580316960811615, -0.7997867465019226],
-          [0.580316960811615, 1.0, 0.024736011400818825],
-          [-0.7997867465019226, 0.024736011400818825, 1.0]
-        ]
-      >
-
-      iex> Scholar.Covariance.correlation_matrix(Nx.tensor([[3, 6], [2, 3], [7, 9], [5, 3]]))
-      #Nx.Tensor<
-        f32[2][2]
-        [
-          [1.0, 0.6673083305358887],
-          [0.6673083305358887, 1.0]
-        ]
-      >
-
-      iex> Scholar.Covariance.correlation_matrix(Nx.tensor([[3, 6, 5], [26, 75, 3], [23, 4, 1]]),
-      ...>   biased: false
-      ...> )
-      #Nx.Tensor<
-        f32[3][3]
-        [
-          [1.0, 0.5803170204162598, -0.7997867465019226],
-          [0.5803170204162598, 1.0, 0.024736013263463974],
-          [-0.7997867465019226, 0.024736013263463974, 1.0]
-        ]
-      >
-  """
-
-  deftransform correlation_matrix(x, opts \\ []) do
-    correlation_matrix_n(x, NimbleOptions.validate!(opts, @opts_schema))
-  end
-
-  defnp correlation_matrix_n(x, opts) do
-    variances =
-      if opts[:biased] do
-        Nx.variance(x, axes: [0])
-      else
-        Nx.variance(x, axes: [0], ddof: 1)
-      end
-
-    Scholar.Covariance.covariance_matrix(x, opts) /
-      Nx.sqrt(Nx.new_axis(variances, 1) * Nx.new_axis(variances, 0))
-  end
-end