MATLAB rands.m函数部分注释

function out1 = rands(in1,in2,in3,in4,in5,in6)
%  BOILERPLATE_START
%  This code is the same for all Weight/Bias Initialization Functions.
persistent INFO;
if isempty(INFO)
  if nnet.test.flags('initWeightInfoTestFcns')
    INFO = nnet.test.initWeightInfoTestFcns;
  else
    INFO = get_info;
  end
end
if (nargin < 1), error(message('nnet:Args:NotEnough')); end
if ischar(in1)
%若in1为字符数组
  switch lower(in1)
  %将字符变为小写
    case 'info', out1 = INFO;
    case 'configure'
      out1 = configure_weight(in2);
    case 'initialize'
      switch(upper(in3))
      case {'IW'}
        if INFO.initInputWeight
          if in2.inputConnect(in4,in5)
            out1 = initialize_input_weight(in2,in4,in5,in6);
          else
            out1 = [];
          end
        else
            nnerr.throw([upper(mfilename) ' does not initialize input weights.']);
        end
      case {'LW'}
        if INFO.initLayerWeight
          if in2.layerConnect(in4,in5)
            out1 = initialize_layer_weight(in2,in4,in5,in6);
          else
            out1 = [];
          end
        else
          nnerr.throw([upper(mfilename) ' does not initialize layer weights.']);
        end
      case {'B'}
        if INFO.initBias
          if in2.biasConnect(in4)
            out1 = initialize_bias(in2,in4);
          else
            out1 = [];
          end
        else
          nnerr.throw([upper(mfilename) ' does not initialize biases.']);
        end
      otherwise,
        error(message('nnet:Args:UnrecValue'));
      end
    otherwise
      try
        out1 = eval(['INFO.' in1]);
      catch me,
        nnerr.throw(['Unrecognized first argument: ''' in1 ''''])
      end
    end
  else
  %若不为字符数组
    if (nargin == 1)
    %若输入只有1个
      if INFO.initFromRows
        out1 = new_value_from_rows(in1);
        %输出in1-by-1的[-1 1]的均匀分布随机矩阵
      else
        nnerr.throw([upper(mfilename) ' cannot initialize from rows.']);
      end
    elseif (nargin == 2)
    %若输入有两个
      if numel(in2) == 1
      %若in2数组中只有1个元素
        if INFO.initFromRowsCols
          out1 = new_value_from_rows_cols(in1,in2);
          %输出in1-by-in2的[-1 1]的均匀分布随机矩阵
        else
          nnerr.throw([upper(mfilename) ' cannot initialize from rows and columns.']);
        end
      elseif size(in2,2) == 2
      %若in2数组有2列
        if INFO.initFromRowsRange
          out1 = new_value_from_rows_range(in1,minmax(in2));
          %minmax每个行向量的最小最大值
          %输出in1-by-size(minmax(in2),1)尺寸的[-1 1]的均匀分布随机矩阵
        else
          nnerr.throw([upper(mfilename) ' cannot initialize from rows and ranges.']);
        end
      elseif size(in2,2) > 2
      %若in2数组不止2列
        if INFO.initFromRowsInput
          out1 = new_value_from_rows_inputs(in1,minmax(in2));
          %输出in1-by-size(minmax(in2),1)尺寸的[-1 1]的均匀分布随机矩阵
        else
          nnerr.throw([upper(mfilename) ' cannot initialize from rows and inputs.']);
        end
      else
        error(message('nnet:rands:SecondArgNotScalarOr2Col'));
      end
    else
      error(message('nnet:Args:TooManyInputArgs'));
    end
  end
end

%  BOILERPLATE_END
%%------------------------------------------------
function info = get_info
  info = nnfcnWeightInit(mfilename,'Random Symmetric',7.0,...
    true,true,true, true,true,true,true, true);
end

function settings = configure_weight(inputs)
  settings = struct;
end

function w = initialize_input_weight(net,i,j,config)
  w = 2*rand(net.inputWeights{i,j}.size)-1;
end

function w = initialize_layer_weight(net,i,j,config)
  w = 2*rand(net.layerWeights{i,j}.size)-1;
end

function b = initialize_bias(net,i)
  b = 2*rand(net.layers{i}.size,1)-1;
end

function x = new_value_from_rows(rows)
  x = 2*rand(rows,1)-1;
end

function x = new_value_from_rows_cols(rows,cols)
  x = 2*rand(rows,cols)-1;
end

function x = new_value_from_rows_range(rows,range)
  x = new_value_from_rows_cols(rows,size(range,1));
end

function x = new_value_from_rows_inputs(rows,inputs)
  x = new_value_from_rows_cols(rows,size(inputs,1));
end

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来源： https://blog.csdn.net/singingbell/article/details/104573790