论文翻译
更新时间:2024-01-16 06:37:01 阅读量: 教育文库 文档下载
hintonwb
function hintonwb(w,b,max_m,min_m)
% HINTONWB Hinton graph of weight matrix and bias vector. % Syntax
% hintonwb(W,b,maxw,minw) % Description
% HINTONWB(W,B,M1,M2) % W - SxR weight matrix % B - Sx1 bias vector.
% MAXW - Maximum weight, default = max(max(abs(W))). % MINW - Minimum weight, default = M1/100.
%and displays a weight matrix and a bias vector represented as a grid of squares.
% Each square's AREA represents a weight's magnitude. % Each square's COLOR represents a weight's sign. % RED for negative weights, GREEN for positive. % Examples
% W = rands(4,5); % b = rands(4,1); % hintonwb(W,b) % See also HINTONW.
% DEFINE BOX EDGES(code is omited) % DEFINE POSITIVE BOX % DEFINE POSITIVE BOX
hintonw
function hintonw(w,max_m,min_m)
% HINTONW Hinton graph of weight matrix. % Syntax
% hintonw(W,maxw,minw) % Description
% HINTONW(W,MAXW,MINW) takes these inputs, % W - SxR weight matrix
% MAXW - Maximum weight, default = max(max(abs(W))). % MINW - Minimum weight, default = M1/100.
1
% and displays a weight matrix represented as a grid of squares. % Each square's AREA represents a weight's magnitude. % Each square's COLOR represents a weight's sign. % RED for negative weights, GREEN for positive. % Examples
% W = rands(4,5); % hintonw(W)
% See also HINTONWB.
if nargin < 1,error('NNET:Arguments','Not enough input arguments.');end if nargin < 2, max_m = max(max(abs(w))); end if nargin < 3, min_m = max_m / 100; end
if max_m == min_m, max_m = 1; min_m = 0; end TFINE BOX EDGES xn1 = [-1 -1 +1]*0.5; xn2 = [+1 +1 -1]*0.5; yn1 = [+1 -1 -1]*0.5; yn2 = [-1 +1 +1]*0.5;
% DEFINE POSITIVE BOX xn = [-1 -1 +1 +1 -1]*0.5; yn = [-1 +1 +1 -1 -1]*0.5; % DEFINE POSITIVE BOX
plotep
function hh = plotep(w,b,e,h)
%PLOTEP Plot a weight-bias position on an error surface. % Syntax
% h = plotep(w,b,e) % h = plotep(w,b,e,h) % Description
% PLOTEP is used to show network learning on a plot % already created by PLOTES.
% PLOTEP(W,B,E) takes these arguments % W - Current weight value. % B - Current bias value. % E - Current error.
% and returns a vector H containing information for % continuing the plot.
2
% PLOTEP(W,B,E,H) continues plotting using the vector H, % returned by the last call to PLOTEP.
% H contains handles to dots plotted on the error surface, so they can be deleted next time, as well as points on the error contour, so they can be connected.
% See also ERRSURF, PLOTES.
% GET LAST POSITION(code is omited) % MOVE MARKERS
% CONNECT NEW POSITION
plotes
function plotes(wv,bv,es,v)
%PLOTES Plot the error surface of a single input neuron. % Syntax
% plotes(wv,bv,es,v) % Description
% PLOTES(WV,BV,ES,V) takes these arguments, % WV - 1xN row vector of values of W. % BV - 1xM row vector of values of B. % ES - MxN matrix of error vectors. % V - View, default = [-37.5, 30].
% and plots the error surface with a contour underneath. % Calculate the error surface ES with ERRSURF. % Examples % p = [3 2]; % t = [0.4 0.8];
% wv = -4:0.4:4; bv = wv;
% ES = errsurf(p,t,wv,bv,'logsig'); % plotes(wv,bv,ES,[60 30]) % See also ERRSURF. % SURFACE
sh = surf(wv,bv,es,scolor); hold on
sh = surf(wv,bv,zeros(length(wv),length(bv))+surfpos,scolor); set(sh,'edgecolor',[0.5 0.5 0.5]) % ERROR GOAL % TITLES
3
xlabel('Weight W'); ylabel('Bias B');
zlabel('Sum Squared Error') title('Error Surface') % WEIGHT & BIAS
set(gca,'xlim',[min(wv),max(wv)]) set(gca,'ylim',[min(bv),max(bv)]) zlim = get(gca,'zlim'); % VIEW
if nargin == 4, view(v), end set(gca,'zlim',[surfpos maxe]); % RIGHT 2-D PLOT subplot(1,2,2); % SURFACE
sh = surf(wv,bv,es*0,scolor); hold on
set(sh,'edgecolor',[0.5 0.5 0.5]) % CONTOUR
[cc,ch] = contour(wv,bv,es,12); hold off
set(ch,'edgecolor',[1 1 1]) % TITLES
xlabel('Weight W'); ylabel('Bias B'); title('Error Contour') % VIEW view([0 90])
set(gca,'xlim',[min(wv) max(wv)]) set(gca,'ylim',[min(bv) max(bv)]) % COLOR colormap(cool);
plotpc
function h = plotpc(w,b,hh)
%PLOTPC Plot a classification line on a perceptron vector plot. % Syntax
% plotpc(W,b)
4
% plotpc(W,b,h) % Description
% PLOTPC(W,B) takes these inputs,
% W - SxR weight matrix (R must be 3 or less). % B - Sx1 bias vector.
% and returns a handle to a plotted classification line. % PLOTPC(W,B,H) takes these inputs, % H - Handle to last plotted line.
% and deletes the last line before plotting the new one. This function does
not change the current axis and is intended to be called after PLOTPV. % Example
% The code below defines and plots the inputs and targets for a perceptron:
% p = [0 0 1 1; 0 1 0 1]; % t = [0 0 0 1]; % plotpv(p,t)
% The following code creates a perceptron with inputs ranging over the values in P, assigns values to its weights and biases, and plots the resulting classification line.
% net = newp(minmax(p),1); % net.iw{1,1} = [-1.2 -0.5]; % net.b{1} = 1;
% plotpc(net.iw{1,1},net.b{1}) % See also PLOTPV. %ERROR CHECKING % PLOTTING % 2-D PLOT % 3-D PLOT
plotperform
function result = plotperform(varargin)
%PLOTPERFORM Plot network performance. % Syntax
% plotperform(tr) % Description
% PLOTPERFORM(TR) plots the training, validation and test
performances given the training record TR returned by the function
5
TRAIN. % Example
% load simplefit_dataset
% net = newff(simplefitInputs,simplefitTargets,20); % [net,tr] = train(net,simplefitInputs,simplefitTargets); % plotperform(tr); % See also plottrainstate % Plot
% New Figure
plotpv
function plotpv(p,t,v)
%PLOTPV Plot perceptron input/target vectors. % Syntax
% plotpv(p,t) % plotpv(p,t,v) % Description
% PLOTPV(P,T) take these inputs,
% P - RxQ matrix of input vectors (R must be 3 or less).
% T - SxQ matrix of binary target vectors (S must be 3 or less). % and plots column vectors in P with markers based on T. % PLOTPV(P,T,V) takes an additional input,
% V - Graph limits = [x_min x_max y_min y_max] % and plots the column vectors with limits set by V. % Example
% The code below defines and plots the inputs and targets for a perceptron:
% p = [0 0 1 1; 0 1 0 1]; % t = [0 0 0 1]; % plotpv(p,t)
% The following code creates a perceptron with inputs ranging over the
values in P, assigns values to its weights and biases, and plots the resulting classification line. % net = newp(minmax(p),1); % net.iw{1,1} = [-1.2 -0.5]; % net.b{1} = 1;
% plotpc(net.iw{1,1},net.b{1})
6
% See also PLOTPC. % ERROR CHECKING % DEFAULTS % MARKERS % PLOT SET UP
plotsomhits
function result = plotsomhits(varargin)
%PLOTSOMHITS Plot self-organizing map sample hits. % Syntax
% plotsomhits(net,inputs)
% plotsomhits(net,inputs,targets) % Description
% PLOTSOMHITS(NET,INPUTS) plots a SOM layer, with each neuron
showing the number of input vectors that it classifies. The relative number of vectors for each neuron is shown via the size of a colored patch. % Example
% load iris_dataset
% net = newsom(irisInputs,[5 5]); % [net,tr] = train(net,irisInputs); % plotsomhits(net,irisInputs); % See also plotsomplanes
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end % Info % Plot
v1 = varargin{1};
if isa(v1,'network') && (nargin == 2) % User arguments - New plot [net,inputs] = deal(varargin{:}); fig = new_figure('');
elseif (isa(v1,'network') && (nargin == 3))
% Standard Plotting Function Arguments - Recycle plot % New Figure
% Standardize inputs % Clear Figure
7
plotsompos
function result = plotsompos(varargin)
%PLOTSOMPOS Plot self-organizing map weight positions. % Syntax
% plotsomtop(net)
% plotsomtop(net,inputs) % Description
% PLOTSOMPOS(NET) plots the input vectors as green dots, and shows %how the SOM classifies the input space by showing blue-gray dots for each
% neuron's weight vector and connecting neighboring neurons with red lines. % Example
% load simplecluster_dataset
% net = newsom(simpleclusterInputs,[10 10]); % net = train(net,simpleclusterInputs); % plotsompos(net,simpleclusterInputs);
% See also plotsomnd, plotsomplanes, plotsomhits
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end % Info % Plot
v1 = varargin{1};
if (nargin < 3) || (~ isa(varargin{2},'struct')) % User arguments - New plot
% Standard Plotting Function Arguments - Recycle plot %Update Figure % Plot Figure
function plot_figure(fig,net,inputs) % Standardize inputs % Setup neurons %Inputs
if ~isempty(inputs) % Weights %Clear Figure
plotsomtop
8
function result = plotsomtop(varargin)
%PLOTSOMTOP Plot self-organizing map topology. % Syntax
% plotsomtop(net) % Description
% PLOTSOMTOP(NET) plots the topology of a SOM layer. % Example
% load iris_dataset
% net = newsom(irisInputs,[8 8]); % plotsomtop(net);
% See also plotsomnd, plotsomplanes, plotsomhits % Plot
% Standard Plotting Function Arguments - Recycle plot % New Figure % Update Figure % Plot Figure
function plot_figure(fig,net,inputs) % Standardize inputs % Clear Figure
plotsom
function plotsom(w,d,nd)
%PLOTSOM Plot self-organizing map. % Syntax
% plotsom(pos) % plotsom(W,d,nd) % Description
% PLOTSOM(POS) takes one argument,
% POS - NxS matrix of S N-dimension neural positions.
% and plots the neuron positions with red dots, linking the neurons within
a Euclidean distance of 1.
% PLOTSOM(W,D,ND) takes three arguments, % W - SxR weight matrix. % D - SxS distance matrix.
% ND - Neighborhood distance, default = 1.
% and plots the neuron's weight vectors with connections between weight
vectors whose neurons are within a distance of 1.
9
% Examples
% Here are some neat plots of various layer topologies: % pos = hextop(5,6); plotsom(pos) % pos = gridtop(4,5); plotsom(pos) % pos = randtop(18,12); plotsom(pos) % pos = gridtop(4,5,2); plotsom(pos) % pos = hextop(4,4,3); plotsom(pos)
% See NEWSOM for an example of plotting a layer's weight vectors with the input vectors they map.
% See also NEWSOM, LEARNSOM, INITSOM. % Arguments
% Check Dimensions % Line coordinates % Plot
plottrainstate
function result = plottrainstate(varargin)
%PLOTTRAINSTATE Plot training state values. % Syntax
% plottrainstate(tr) % Description
% PLOTTRAINSTATE(TR) plots the training state from a training record
TR returned by TRAIN. % Example
% load housing
% net = newff(p,t,20); % [net,tr] = train(net,p,t); % plottrainstate(tr); % See also plottrainstate
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end % Info % Plot
if nargin == 1
% User arguments - New plot tr = varargin{1}; fig = new_figure(''); else
10
% Standard Plotting Function Arguments - Recycle plot % New Figure % Update Figure % Update Figure
function clear_figure(fig) drawnow
plotvec
function hh = plotvec(x,c,m)
%PLOTVEC Plot vectors with different colors. % Syntax
% plotvec(x,c,m) % Description
% PLOTVEC(X,C,M) takes these inputs, % X - Matrix of (column) vectors. % C - Row vector of color coordinate. % M - Marker, default = '+'.
% and plots each ith vector in X with a marker M, using the ith value in C
as the color coordinate.
% PLOTVEC(X) only takes a matrix X and plots each ith vector in X
with marker '+' using the index i as the color coordinate. % Examples
% x = [0 1 0.5 0.7; -1 2 0.5 0.1]; % c = [1 2 3 4]; % plotvec(x,c)
%if nargin < 1,error('NNET:Arguments','Not enough input arguments.'); end % VECTORS [xr,xc] = size(x); if xr < 2
x = [x; zeros(2-xr,xc)]; xr = 2; end
% COLORS % MARKER
% 2-D PLOTTING % 3-D PLOTTING
11
plotv
function plotv(m,t)
%PLOTV Plot vectors as lines from the origin. % Syntax
% plotv(m,t) % Description
% PLOTV(M,T) takes two inputs,
% M - RxQ matrix of Q column vectors with R elements. % T - (optional) the line plotting type, default = '-'. % and plots the column vectors of M.
% R must be 2 or greater. If R is greater than two, % only the first two rows of M are used for the plot. % Examples
% plotv([-.4 0.7 .2; -0.5 .1 0.5],'-')
%if nargin < 1,error('NNET:Arguments','Wrong number of arguments.');end
12
Hintonwb翻译
function hintonwb(w,b,max_m,min_m) %hinton曲线表示权矩阵和的偏移矢量 %语法
% hintonwb(W,b,maxw,minw) % 描述
% HINTONWB(W,B,M1,M2) % W - SxR 权矩阵 % B - Sx1 偏移矢量
%MAXW -最大权重, 定义 = max(max(abs(W)))。MINW -最小权重, 定%义 = M1/100.显示一个权矩阵和一个偏移矢量代替. 作为一个矩形方 %块.每个矩形方块的面积是权重的大小.每个矩形方块的颜色是权重的标%志.红色代表负权重,绿色代表正权重. %如例
% W = 区间为(4,5); % b = 区间为(4,1); % hintonwb(W,b)
% 也可以参考HINTONW函数 %定义格子的边界(代码略去) %定义在格子里的 %定义在格子里的
hintonw翻译
function hintonw(w,max_m,min_m) %hinton权矩阵函数图形. %语法
% hintonw(W,maxw,minw) % 描述
% HINTONW(W,MAXW,MINW) 将这些函数输入 % W - SxR weight matrix
% MAXW - 最大权重, 默认值= max(max(abs(W))). % MINW -最小权重 默认值= M1/100.
% 显示一个方形的矩阵函数图形。每个矩形的面积代表权重的大小。每 %个矩形的颜色代表权重信号。红色代表负权重,绿色代表正权重。 %例如
13
% W = 区间为(4,5); % hintonw(W)
%也可参考HINTONWB函数.
if nargin < 1,error('NNET:Arguments','Not enough input arguments.');end if nargin < 2, max_m = max(max(abs(w))); end if nargin < 3, min_m = max_m / 100; end
if max_m == min_m, max_m = 1; min_m = 0; end %定义格子的边界 xn1 = [-1 -1 +1]*0.5; xn2 = [+1 +1 -1]*0.5; yn1 = [+1 -1 -1]*0.5; yn2 = [-1 +1 +1]*0.5; %定义在格子里的
xn = [-1 -1 +1 +1 -1]*0.5; yn = [-1 +1 +1 -1 -1]*0.5; %定义在格子里的
plotep翻译
function hh = plotep(w,b,e,h)
%在一个误差的平面绘制PLOTEP函数的权偏移变量的位置. %语法
% h = plotep(w,b,e) % h = plotep(w,b,e,h) % 描述
% 在网络学习显示之前绘制PLOTEP函数 % PLOTEP(W,B,E) 以这些论点 % W -通常的权值 % B -通常的偏离值 % E -通常的错误
% 返回矢量H包含的信息是持续的绘图. % PLOTEP(W,B,E,H) 持续用矢量H绘图,最后返回PLOTEP函数.矢
量H包换点的绘制在误差的平面,所以他们可以下次删除,同样也可以点出误差的轮廓,银次他们可以被连接. % 也可以参考ERRSURF,PLOTES函数.
if nargin < 3, error('NNET:Arguments','Not enough input arguments'),end % 得到最后的位置 if nargin == 4
14
w2 = h(1); b2 = h(2);
delete(h(3:length(h))); end
%移动标记 %连接新的位置
plotes翻译
function plotes(wv,bv,es,v)
%绘制PLOTES函数单独的神经元输入在一个误差平面式. % 语法
% plotes(wv,bv,es,v) % 描述
% PLOTES(WV,BV,ES,V) 以这些论点, % WV - 1xN 行向量的价值是 W. % BV - 1xM 行向量的价值是B. % ES - MxN 误差向量的矩阵. % V -查看, 定义 = [-37.5, 30].
% 划分这个误差的平面以下平面的轮廓.计算这个误差平面ES用ERRSURF函数. % 例如
% p = [3 2]; % t = [0.4 0.8];
% wv = -4:0.4:4; bv = wv;
% ES = errsurf(p,t,wv,bv,'logsig'); % plotes(wv,bv,ES,[60 30]) % 参考ERRSURF函数. % 左边的3D绘制 subplot(1,2,1);
[px,py] = gradient(es,wv,bv); scolor = sqrt(px.^2+py.^2); %表面
sh = surf(wv,bv,es,scolor); hold on
sh = surf(wv,bv,zeros(length(wv),length(bv))+surfpos,scolor); set(sh,'edgecolor',[0.5 0.5 0.5]) %误差的目标
15
%主题
xlabel('Weight W'); ylabel('Bias B');
zlabel('Sum Squared Error') title('Error Surface') %权重&偏移
set(gca,'xlim',[min(wv),max(wv)]) set(gca,'ylim',[min(bv),max(bv)]) zlim = get(gca,'zlim'); %查看
if nargin == 4, view(v), end set(gca,'zlim',[surfpos maxe]); %正确的2D绘图 subplot(1,2,2); %表面
sh = surf(wv,bv,es*0,scolor); hold on
set(sh,'edgecolor',[0.5 0.5 0.5]) %轮廓
[cc,ch] = contour(wv,bv,es,12); hold off
set(ch,'edgecolor',[1 1 1]) %主题
xlabel('Weight W'); ylabel('Bias B'); title('Error Contour') %观点 view([0 90])
set(gca,'xlim',[min(wv) max(wv)]) set(gca,'ylim',[min(bv) max(bv)]) %颜色
colormap(cool);
plotpc翻译
function h = plotpc(w,b,hh)
%PLOTPC Plot a classification line on a perceptron vector plot. % 绘制PLOTPC函数模块的划分线和感知向量
16
% Syntax %语法
% plotpc(W,b) % plotpc(W,b,h) %描述
% PLOTPC(W,B) 将这些变量输入,
% W - SxR权重矩阵(R的值必须是3或更小). % B - Sx1偏移向量. % 然后返回处理绘制分类线
% PLOTPC(W,B,H) 将这些变量输入, % H -处理最后的绘制线.
% 在绘制新线之前删掉最后的线。这个函数不能改变当前的当前的轴,其目的是在PLOTPV之后访问 % 例如
%感知器的目标是输入下面的代码定义和绘制 % p = [0 0 1 1; 0 1 0 1]; % t = [0 0 0 1]; % plotpv(p,t)
% 以下的代码运用排列测量值P创造一个感知器,指定值的偏重和偏差,绘制结果产生的分类。 % net = newp(minmax(p),1); % net.iw{1,1} = [-1.2 -0.5]; % net.b{1} = 1;
% plotpc(net.iw{1,1},net.b{1}) %参考函数PLOTPV %检查误差 %默认值 %绘制中 -绘制 =绘制
plotperform翻译
function plotpv(p,t,v)
%绘制PLOTPV函数感知器输入或目标向量 % 语法
% plotpv(p,t) % plotpv(p,t,v)
17
% 描述
% PLOTPV(P,T) 将这些变量输入,
% P - RxQ 输入函数的矩阵(R的值必须是3或比3小).
% T - SxQ二进制目标向量的矩阵(S的值必须是3或比3小). % 根据T向量在P函数里绘制列向量, % PLOTPV(P,T,V) 将一个变量额外的输入, % V -图的限制= [x_min x_max y_min y_max] % 用nd绘制圆向量的被V函数限制 %例如
% 在代码下面输入定义和绘制目标作为感知器 % p = [0 0 1 1; 0 1 0 1]; % t = [0 0 0 1]; % plotpv(p,t)
% 以下的代码运用排列测量值P创造一个感知器,指定值的偏重和偏差,绘制结果所产生的分类。 % net = newp(minmax(p),1); % net.iw{1,1} = [-1.2 -0.5]; % net.b{1} = 1;
% plotpc(net.iw{1,1},net.b{1}) %参考PLOTPC函数 %输入错误
if nargin < 2, error('NNET:Arguments','Not enough arguments.'),end [pr,pc] = size(p); [tr,tc] = size(t);
if (pr > 3), error('NNET:Arguments','P must 1, 2, or 3 rows.'), end if tr > 3, error('NNET:Arguments','T must have 1, 2, or 3 rows.'), end %默认值 %标记
marker = ['ob';'or';'*b';'*r';'+b';'+r';'xb';'xr']; %绘制中 for i=1:pc
m = marker([4 2 1]*t(:,i)+1,:); plot3(p(1,i),p(2,i),p(3,i),m) hold on end
%绘制装载
18
plotsomhits翻译
function result = plotsomhits(varargin)
%绘制PLOTSOMHITS函数的自我组织映射的样品命中 %语法
% plotsomhits(net,inputs)
% plotsomhits(net,inputs,targets) % 描述
% PLOTSOMHITS(NET,INPUTS) 绘制一个SOM函数的层次,用每个神经元数目的输入向量表示分类,相对数目的向量引导每个神经元是通过一个色彩的补丁 % 例如
% load iris_dataset
% net = newsom(irisInputs,[5 5]); % [net,tr] = train(net,irisInputs); % plotsomhits(net,irisInputs); %参考函数plotsomplanes
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end %信息 %绘制
v1 = varargin{1};
if isa(v1,'network') && (nargin == 2) % 用户参数-新的绘制
[net,inputs] = deal(varargin{:}); fig = new_figure('');
elseif (isa(v1,'network') && (nargin == 3)) % 标准的绘制运行参数-循环绘制 %新图形 %修正图形 %绘制图形
function plot_figure(fig,net,inputs) %标准化验证 %清理图形
plotsompos翻译
function result = plotsompos(varargin).
19
% 绘制PLOTSOMPOS函数自组织映射权函数位置 %语法
% plotsomtop(net)
% plotsomtop(net,inputs) %描述
% PLOTSOMPOS(NET) 绘制输入向量用绿色的点,显示怎么分类SOM函数输入空格是蓝灰色点,每个神经元的权重矢量和链接边上神经元用红线划分 % 例如
% load simplecluster_dataset
% net = newsom(simpleclusterInputs,[10 10]); % net = train(net,simpleclusterInputs); % plotsompos(net,simpleclusterInputs);
%参考plotsomnd函数, plotsomplanes函数, plotsomhits函数
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end %信息 %绘制
v1 = varargin{1};
if (nargin < 3) || (~ isa(varargin{2},'struct')) % 用户参数-新的绘制
varargin = fill_defaults(varargin,[]); [net,inputs] = deal(varargin{:}); fig = new_figure('');
elseif (isa(v1,'network') && (nargin == 3)) % 开始绘制函数参数 –循环绘制 [net,tr,signals] = deal(varargin{:}); %新图像 %修正图像 %绘制图像
function plot_figure(fig,net,inputs) %标准化输入 %安装神经元 %输入 %连接 %权重 %轴线
if numDimensions == 2 set(ud.axis,'view',[0 90])
20
end
%清理图形
plotsomtop翻译
function result = plotsomtop(varargin)
%绘制PLOTSOMTOP函数自组织映射的拓扑结构 %语法
% plotsomtop(net) %描述
% PLOTSOMTOP(NET) 绘制SOM层的拓扑结构. %例如
% load iris_dataset
% net = newsom(irisInputs,[8 8]); % plotsomtop(net);
%参考plotsomnd函数, plotsomplanes函数, plotsomhits函数
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end %信息 %绘制
v1 = varargin{1};
if (isa(v1,'network') && (nargin == 1)) % User arguments - New plot [net,inputs] = deal(varargin{:}); fig = new_figure('');
elseif (isa(v1,'network') && (nargin == 3)) % 标准的绘制运行参数-循环绘制 %新图形 %修正图形 %绘制图形
function plot_figure(fig,net,inputs) %标准化验证 % 安装神经元 %清理图像
plotsom翻译
21
function plotsom(w,d,nd)
%绘制PLOTSOM函数自组织映射图网路 %语法
% plotsom(pos) % plotsom(W,d,nd) %描述
% PLOTSOM(POS) 拿出一个变量,
% POS - NxS matrix of S N-矩阵的神经元位置. % S N-dimension
%用红色的点绘制神经元的位置,连接神经元的欧式距离为1 % PLOTSOM(W,D,ND) 取出三个变量, % W - SxR 权矩阵. % D - SxS 距离矩阵. % ND – 接近的距离, default = 1.
%绘制神经元权向量,并且连接权重向量的神经元距离在1之内 % 例如
% 这里绘制了各种拓扑结构
% pos = hextop(5,6); plotsom(pos) % pos = gridtop(4,5); plotsom(pos) % pos = randtop(18,12); plotsom(pos) % pos = gridtop(4,5,2); plotsom(pos) % pos = hextop(4,4,3); plotsom(pos) %参考NEWSOM函数,例如绘制一个分成的权重向量依靠输入向量的图纸
%回去参考NEWSOM函数, LEARNSOM函数, INITSOM函数 %参数
%检测大小 %坐标排成一排 %绘制
plottrainstate翻译
function result = plottrainstate(varargin)
%绘制PLOTTRAINSTATE函数的训练状态值 % 语法
% plottrainstate(tr) % 描述
22
%PLOTTRAINSTATE(TR) 从TR函数中记录和绘制训练状态然后返回训练
% 例如
% load housing
% net = newff(p,t,20); % [net,tr] = train(net,p,t); % plottrainstate(tr); %参考函数plottrainstate
if nargin < 1, error('NNET:Arguments','Not enough input arguments.'); end % 信息 %绘制
if nargin == 1
% 用户参数-新的绘制 tr = varargin{1};
fig = new_figure(''); else
%标准的绘制运行参数-循环绘制 %新图形 %修正图形 %修正图形
function clear_figure(fig) drawnow
plotvec翻译
function hh = plotvec(x,c,m)
%用不同颜色绘制PLOTVEC函数向量 %语法
% plotvec(x,c,m) %描述
% PLOTVEC(X,C,M) 将这些变量输入, % X -矩阵向量.
% C -行向量的颜色协调. % M –标记, default = '+'.
% 绘制每一个向量标记一个M,用C的值作为颜色的来协调
% PLOTVEC(X) 只需要一个矩阵X,绘制每一个矩阵X都需要标
23
记’+’,并指出i作为颜色协调 % 例如
% x = [0 1 0.5 0.7; -1 2 0.5 0.1]; % c = [1 2 3 4]; % plotvec(x,c)
if nargin < 1,error('NNET:Arguments','Not enough input arguments.'); end %向量
[xr,xc] = size(x); if xr < 2
x = [x; zeros(2-xr,xc)]; xr = 2; end %颜色 %标志 -绘制 =绘制
plotv翻译
function plotv(m,t)
%绘制PLOTV函数载体线的起源 % 语法
% plotv(m,t) % 描述
% PLOTV(M,T) 将这两个变量输入, % M - RxQ矩阵和列向量R的原理
% T - (optional)线绘制的种类和原理, 默认值 = '-'. % 绘制列向量M
% R的值是2或比2大。如果R比2大只有第一个的前两排是用M来绘制的 % 例如
% plotv([-.4 0.7 .2; -0.5 .1 0.5],'-')
24
正在阅读:
论文翻译01-16
超声波自动蔽障小车代码05-09
2020年在全市审计系统党风廉政建设工作会议上的报告09-06
食品化学复习题及答案05-07
120T汽车吊参数09-05
西华大学发动机课程设计说明书(含程序)05-22
线性常微分方程组06-08
2020年精选淘宝客服工作计划03-03
- exercise2
- 铅锌矿详查地质设计 - 图文
- 厨余垃圾、餐厨垃圾堆肥系统设计方案
- 陈明珠开题报告
- 化工原理精选例题
- 政府形象宣传册营销案例
- 小学一至三年级语文阅读专项练习题
- 2014.民诉 期末考试 复习题
- 巅峰智业 - 做好顶层设计对建设城市的重要意义
- (三起)冀教版三年级英语上册Unit4 Lesson24练习题及答案
- 2017年实心轮胎现状及发展趋势分析(目录)
- 基于GIS的农用地定级技术研究定稿
- 2017-2022年中国医疗保健市场调查与市场前景预测报告(目录) - 图文
- 作业
- OFDM技术仿真(MATLAB代码) - 图文
- Android工程师笔试题及答案
- 生命密码联合密码
- 空间地上权若干法律问题探究
- 江苏学业水平测试《机械基础》模拟试题
- 选课走班实施方案
- 幸福美丽家园建设的心得体会
- 2013-2014学年安徽省淮北市五校2014届九年级上学期联考物理试题
- 移梁作业指导书
- 《中学生学科素养培养研究》课题实施方案
- 2002全国市场研究协会文集
- 生命的壮歌教学设计
- 河北省2012定额费用标准讲解
- 关于在京建筑施工企业建立农民工工资支付保障制度的通告
- 在省市人大代表联合视察活动意见反馈会上的讲话
- 通信原理期末复习题答案复习资料
- 汽车起重机起重性能表(主臂)
- 华中科技大学 微积分 极限习题课及答案
- 铜文化定稿
- LS-DYNA中文教程
- 高中物理电磁学知识高考前必看总结
- 未成年人思想道德建设中家庭教育的优化与完善
- 教师编制简答题论述题汇编
- 令人困惑的马普所
- 地毯的毛纱染色工程
- 基于Matlab的纯滞后控制系统设计 - 图文