fix identation spaces in markdown

Author: OkonSamuel

src/models/decomposition_models.jl

@@ -303,11 +303,11 @@ Train the machine using `fit!(mach, rows=...)`.
 # Hyper-parameters
 
 - `maxoutdim=0`: Together with `variance_ratio`, controls the output dimension `outdim`
-   chosen by the model. Specifically, suppose that `k` is the smallest integer such that
-   retaining the `k` most significant principal components accounts for `variance_ratio` of
-   the total variance in the training data. Then `outdim = min(outdim, maxoutdim)`. If
-   `maxoutdim=0` (default) then the effective `maxoutdim` is `min(n, indim - 1)` where `n`
-   is the number of observations and `indim` the number of features in the training data.
+  chosen by the model. Specifically, suppose that `k` is the smallest integer such that
+  retaining the `k` most significant principal components accounts for `variance_ratio` of
+  the total variance in the training data. Then `outdim = min(outdim, maxoutdim)`. If
+  `maxoutdim=0` (default) then the effective `maxoutdim` is `min(n, indim - 1)` where `n`
+  is the number of observations and `indim` the number of features in the training data.
 
 - `variance_ratio::Float64=0.99`: The ratio of variance preserved after the transformation
 
@@ -321,38 +321,38 @@ Train the machine using `fit!(mach, rows=...)`.
       dimension and otherwise use `:svd`
 
 - `mean=nothing`: if `nothing`, centering will be computed and applied, if set to `0` no
-    centering (data is assumed pre-centered); if a vector is passed, the centering is done
-    with that vector.
+  centering (data is assumed pre-centered); if a vector is passed, the centering is done
+  with that vector.
 
 # Operations
 
 - `transform(mach, Xnew)`: Return a lower dimensional projection of the input `Xnew`, which
-    should have the same scitype as `X` above.
+  should have the same scitype as `X` above.
 
 - `inverse_transform(mach, Xsmall)`: For a dimension-reduced table `Xsmall`,
-    such as returned by `transform`, reconstruct a table, having same the number
-    of columns as the original training data `X`, that transforms to `Xsmall`.
-    Mathematically, `inverse_transform` is a right-inverse for the PCA projection
-    map, whose image is orthogonal to the kernel of that map. In particular, if
-    `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
-    only an approximation to `Xnew`.
+  such as returned by `transform`, reconstruct a table, having same the number
+  of columns as the original training data `X`, that transforms to `Xsmall`.
+  Mathematically, `inverse_transform` is a right-inverse for the PCA projection
+  map, whose image is orthogonal to the kernel of that map. In particular, if
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
+  only an approximation to `Xnew`.
 
 # Fitted parameters
 
 The fields of `fitted_params(mach)` are:
 
 - `projection`: Returns the projection matrix, which has size `(indim, outdim)`, where
-    `indim` and `outdim` are the number of features of the input and output respectively.
+  `indim` and `outdim` are the number of features of the input and output respectively.
 
 # Report
 
 The fields of `report(mach)` are:
 
 - `indim`: Dimension (number of columns) of the training data and new data to be 
-    transformed.
+  transformed.
 
 - `outdim = min(n, indim, maxoutdim)` is the output dimension; here `n` is the number of
-    observations.
+  observations.
 
 - `tprincipalvar`: Total variance of the principal components.
 
@@ -408,19 +408,19 @@ Train the machine using `fit!(mach, rows=...)`.
 # Hyper-parameters
 
 - `maxoutdim=0`: Controls the the dimension (number of columns) of the output,
-    `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
-    observations and `indim` the input dimension.
+  `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
+  observations and `indim` the input dimension.
 
 - `kernel::Function=(x,y)->x'y`: The kernel function, takes in 2 vector arguments
-    x and y, returns a scalar value. Defaults to the dot product of `x` and `y`.
+  x and y, returns a scalar value. Defaults to the dot product of `x` and `y`.
 
 - `solver::Symbol=:eig`: solver to use for the eigenvalues, one of `:eig`(default, uses
-    `LinearAlgebra.eigen`), `:eigs`(uses `Arpack.eigs`).
+  `LinearAlgebra.eigen`), `:eigs`(uses `Arpack.eigs`).
 
 - `inverse::Bool=true`: perform calculations needed for inverse transform
 
 - `beta::Real=1.0`: strength of the ridge regression that learns the inverse transform
-    when inverse is true.
+  when inverse is true.
 
 - `tol::Real=0.0`: Convergence tolerance for eigenvalue solver.
 
@@ -432,26 +432,26 @@ Train the machine using `fit!(mach, rows=...)`.
     should have the same scitype as `X` above.
 
 - `inverse_transform(mach, Xsmall)`: For a dimension-reduced table `Xsmall`, such as
-    returned by `transform`, reconstruct a table, having same the number of columns as the
-    original training data `X`, that transforms to `Xsmall`.  Mathematically,
-    `inverse_transform` is a right-inverse for the PCA projection map, whose image is
-    orthogonal to the kernel of that map. In particular, if 
-    `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is only an 
-    approximation to `Xnew`.
+  returned by `transform`, reconstruct a table, having same the number of columns as the
+  original training data `X`, that transforms to `Xsmall`.  Mathematically,
+  `inverse_transform` is a right-inverse for the PCA projection map, whose image is
+  orthogonal to the kernel of that map. In particular, if 
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is only an 
+  approximation to `Xnew`.
 
 # Fitted parameters
 
 The fields of `fitted_params(mach)` are:
 
 - `projection`: Returns the projection matrix, which has size `(indim, outdim)`, where
-    `indim` and `outdim` are the number of features of the input and ouput respectively.
+  `indim` and `outdim` are the number of features of the input and ouput respectively.
 
 # Report
 
 The fields of `report(mach)` are:
 
 - `indim`: Dimension (number of columns) of the training data and new data to be
-    transformed.
+  transformed.
 
 - `outdim`: Dimension of transformed data.
 
@@ -498,17 +498,17 @@ In MLJ or MLJBase, bind an instance `model` to data with
 Here:
 
 - `X` is any table of input features (eg, a `DataFrame`) whose columns are of scitype
-    `Continuous`; check column scitypes with `schema(X)`.
+  `Continuous`; check column scitypes with `schema(X)`.
 
 Train the machine using `fit!(mach, rows=...)`.
 
 # Hyper-parameters
 
 - `outdim::Int=0`: The number of independent components to recover, set automatically 
-    if `0`.
+  if `0`.
 
 - `alg::Symbol=:fastica`: The algorithm to use (only `:fastica` is supported at the 
-    moment).
+  moment).
 
 - `fun::Symbol=:tanh`: The approximate neg-entropy function, one of `:tanh`, `:gaus`.
 
@@ -519,18 +519,18 @@ Train the machine using `fit!(mach, rows=...)`.
 - `tol::Real=1e-6`: The convergence tolerance for change in the unmixing matrix W.
 
 - `mean::Union{Nothing, Real, Vector{Float64}}=nothing`: mean to use, if nothing (default)
-    centering is computed and applied, if zero, no centering; otherwise a vector of means 
-    can be passed.
+  centering is computed and applied, if zero, no centering; otherwise a vector of means 
+  can be passed.
 
 - `winit::Union{Nothing,Matrix{<:Real}}=nothing`: Initial guess for the unmixing matrix 
-    `W`: either an empty matrix (for random initialization of `W`), a matrix of size 
-    `m × k` (if `do_whiten` is true), or a matrix of size `m × k`. Here `m` is the number 
-    of components (columns) of the input.
+  `W`: either an empty matrix (for random initialization of `W`), a matrix of size 
+  `m × k` (if `do_whiten` is true), or a matrix of size `m × k`. Here `m` is the number 
+  of components (columns) of the input.
 
 # Operations
 
 - `transform(mach, Xnew)`: Return the component-separated version of input `Xnew`, which 
-    should have the same scitype as `X` above.
+  should have the same scitype as `X` above.
 
 # Fitted parameters
 
@@ -545,7 +545,7 @@ The fields of `fitted_params(mach)` are:
 The fields of `report(mach)` are:
 
 - `indim`: Dimension (number of columns) of the training data and new data to be 
-    transformed.
+  transformed.
 
 - `outdim`: Dimension of transformed data.
 
@@ -636,30 +636,30 @@ Train the machine using `fit!(mach, rows=...)`.
 # Operations
 
 - `transform(mach, Xnew)`: Return a lower dimensional projection of the input `Xnew`, which
-    should have the same scitype as `X` above.
+  should have the same scitype as `X` above.
 
 - `inverse_transform(mach, Xsmall)`: For a dimension-reduced table `Xsmall`,
-    such as returned by `transform`, reconstruct a table, having same the number
-    of columns as the original training data `X`, that transforms to `Xsmall`.
-    Mathematically, `inverse_transform` is a right-inverse for the PCA projection
-    map, whose image is orthogonal to the kernel of that map. In particular, if
-    `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
-    only an approximation to `Xnew`.
+  such as returned by `transform`, reconstruct a table, having same the number
+  of columns as the original training data `X`, that transforms to `Xsmall`.
+  Mathematically, `inverse_transform` is a right-inverse for the PCA projection
+  map, whose image is orthogonal to the kernel of that map. In particular, if
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
+  only an approximation to `Xnew`.
 
 # Fitted parameters
 
 The fields of `fitted_params(mach)` are:
 
 - `projection`: Returns the projection matrix, which has size `(indim, outdim)`, where
-    `indim` and `outdim` are the number of features of the input and ouput respectively.
-    Each column of the projection matrix corresponds to a factor.
+  `indim` and `outdim` are the number of features of the input and ouput respectively.
+  Each column of the projection matrix corresponds to a factor.
 
 # Report
 
 The fields of `report(mach)` are:
 
 - `indim`: Dimension (number of columns) of the training data and new data to be 
-    transformed.
+  transformed.
 
 - `outdim`: Dimension of transformed data (number of factors).
 
@@ -712,61 +712,61 @@ In MLJ or MLJBase, bind an instance `model` to data with
 Here:
 
 - `X` is any table of input features (eg, a `DataFrame`) whose columns are of scitype
-    `Continuous`; check column scitypes with `schema(X)`.
+  `Continuous`; check column scitypes with `schema(X)`.
 
 Train the machine using `fit!(mach, rows=...)`.
 
 # Hyper-parameters
 
 - `maxoutdim=0`: Controls the the dimension (number of columns) of the output,
-    `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
-    observations and `indim` the input dimension.
+  `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
+  observations and `indim` the input dimension.
 
 - `method::Symbol=:ml`: The method to use to solve the problem, one of `:ml`, `:em`,
-    `:bayes`.
+  `:bayes`.
 
 - `maxiter::Int=1000`: The maximum number of iterations.
 
 - `tol::Real=1e-6`: The convergence tolerance.
 
 - `mean::Union{Nothing, Real, Vector{Float64}}=nothing`: If `nothing`, centering will be
-    computed and applied; if set to `0` no centering is applied (data is assumed
-    pre-centered); if a vector, the centering is done with that vector.
+  computed and applied; if set to `0` no centering is applied (data is assumed
+  pre-centered); if a vector, the centering is done with that vector.
 
 # Operations
 
 - `transform(mach, Xnew)`: Return a lower dimensional projection of the input `Xnew`, which
-    should have the same scitype as `X` above.
+  should have the same scitype as `X` above.
 
 - `inverse_transform(mach, Xsmall)`: For a dimension-reduced table `Xsmall`,
-    such as returned by `transform`, reconstruct a table, having same the number
-    of columns as the original training data `X`, that transforms to `Xsmall`.
-    Mathematically, `inverse_transform` is a right-inverse for the PCA projection
-    map, whose image is orthogonal to the kernel of that map. In particular, if
-    `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
-    only an approximation to `Xnew`.
+  such as returned by `transform`, reconstruct a table, having same the number
+  of columns as the original training data `X`, that transforms to `Xsmall`.
+  Mathematically, `inverse_transform` is a right-inverse for the PCA projection
+  map, whose image is orthogonal to the kernel of that map. In particular, if
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is only an 
+  approximation to `Xnew`.
 
 # Fitted parameters
 
 The fields of `fitted_params(mach)` are:
 
 - `projection`: Returns the projection matrix, which has size `(indim, outdim)`, where
-    `indim` and `outdim` are the number of features of the input and ouput respectively.
-    Each column of the projection matrix corresponds to a principal component.
+  `indim` and `outdim` are the number of features of the input and ouput respectively.
+  Each column of the projection matrix corresponds to a principal component.
 
 # Report
 
 The fields of `report(mach)` are:
 
 - `indim`: Dimension (number of columns) of the training data and new data to be 
-    transformed.
+  transformed.
 
 - `outdim`: Dimension of transformed data.
 
 - `tvat`: The variance of the components.
 
 - `loadings`: The models loadings, weights for each variable used when calculating 
-    principal components.
+  principal components.
 
 # Examples