怎么将一组数据归一化到(0，1)之间，用matlab编程

很简单，用函数mapminmax档太长我就不了，只提醒关键

1 默认的map范围是[-1, 1]，如果需要[0, 1]，则按这样的格式提供参数:

MappedData = mapminmax(OriginalData, 0, 1);

2 只按行归一化，如果是矩阵，则每行各自归一化，如果需要对整个矩阵归一化，用如下方法：

FlattenedData = OriginalData(:)'; % 展开矩阵为一列，然后转置为一行。

MappedFlattened = mapminmax(FlattenedData, 0, 1); % 归一化。

MappedData = reshape(MappedFlattened, size(OriginalData)); % 还原为原始矩阵形式。此处不需转置回去，因为reshape恰好是按列重新排序

文档全文如下：

mapminmax

Process matrices by mapping row minimum and maximum values to [-1 1]

Syntax

[Y,PS] = mapminmax(YMIN,YMAX)

[Y,PS] = mapminmax(X,FP)

Y = mapminmax('apply',X,PS)

X = mapminmax('reverse',Y,PS)

dx_dy = mapminmax('dx',X,Y,PS)

dx_dy = mapminmax('dx',X,[],PS)

name = mapminmax('name');

fp = mapminmax('pdefaults');

names = mapminmax('pnames');

remconst('pcheck',FP);

Description

mapminmax processes matrices by normalizing the minimum and maximum values of each row to [YMIN, YMAX].

mapminmax(X,YMIN,YMAX) takes X and optional parameters

X

N x Q matrix or a 1 x TS row cell array of N x Q matrices

YMIN

Minimum value for each row of Y (default is -1)

YMAX

Maximum value for each row of Y (default is 1)

and returns

Y

Each M x Q matrix (where M == N) (optional)

PS

Process settings that allow consistent processing of values

mapminmax(X,FP) takes parameters as a struct: FP.ymin, FP.ymax.

mapminmax('apply',X,PS) returns Y, given X and settings PS.

mapminmax('reverse',Y,PS) returns X, given Y and settings PS.

mapminmax('dx',X,Y,PS) returns the M x N x Q derivative of Y with respect to X.

mapminmax('dx',X,[],PS) returns the derivative, less efficiently.

mapminmax('name') returns the name of this process method.

mapminmax('pdefaults') returns the default process parameter structure.

mapminmax('pdesc') returns the process parameter descriptions.

mapminmax('pcheck',FP) throws an error if any parameter is illegal.

Examples

Here is how to format a matrix so that the minimum and maximum values of each row are mapped to default interval [-1, 1].

*

x1 = [1 2 4; 1 1 1; 3 2 2; 0 0 0]

[y1,PS] = mapminmax(x1)

Next, apply the same processing settings to new values.

*

x2 = [5 2 3; 1 1 1; 6 7 3; 0 0 0]

y2 = mapminmax('apply',x2,PS)

Reverse the processing of y1 to get x1 again.

*

x1_again = mapminmax('reverse',y1,PS)

Algorithm

It is assumed that X has only finite real values, and that the elements of each row are not all equal.

*

y = (ymax-ymin)*(x-xmin)/(xmax-xmin) ymin;

怎样把数据归一化到-1到1

很简单，用函数mapminmax，文档太长我翻译了，只提醒几个关键

1 默map范围是[-1, 1]，如果需要[0, 1]，则按这样的格式提供参数:

MappedData = mapminmax(OriginalData, 0, 1);

2 只按行归一化，如果是矩阵，则每行各自归一化，如果需要对整个矩阵归一化如下方法：

FlattenedData = OriginalData(:)'; % 展开矩阵为一列，然后转置为一行。

MappedFlattened = mapminmax(FlattenedData, 0, 1); % 归一化。

MappedData = reshape(MappedFlattened, size(OriginalData)); % 还原为原始矩阵形式。此处不需转置回去，因为reshape恰好是按列重新排序

文档全文如下：

mapminmax

Process matrices by mapping row minimum and maximum values to [-1 1]

Syntax

[Y,PS] = mapminmax(YMIN,YMAX)

[Y,PS] = mapminmax(X,FP)

Y = mapminmax('apply',X,PS)

X = mapminmax('reverse',Y,PS)

dx_dy = mapminmax('dx',X,Y,PS)

dx_dy = mapminmax('dx',X,[],PS)

name = mapminmax('name');

fp = mapminmax('pdefaults');

names = mapminmax('pnames');

remconst('pcheck',FP);

Description

mapminmax processes matrices by normalizing the minimum and maximum values of each row to [YMIN, YMAX].

mapminmax(X,YMIN,YMAX) takes X and optional parameters

X

N x Q matrix or a 1 x TS row cell array of N x Q matrices

YMIN

Minimum value for each row of Y (default is -1)

YMAX

Maximum value for each row of Y (default is 1)

and returns

Y

Each M x Q matrix (where M == N) (optional)

PS

Process settings that allow consistent processing of values

mapminmax(X,FP) takes parameters as a struct: FP.ymin, FP.ymax.

mapminmax('apply',X,PS) returns Y, given X and settings PS.

mapminmax('reverse',Y,PS) returns X, given Y and settings PS.

mapminmax('dx',X,Y,PS) returns the M x N x Q derivative of Y with respect to X.

mapminmax('dx',X,[],PS) returns the derivative, less efficiently.

mapminmax('name') returns the name of this process method.

mapminmax('pdefaults') returns the default process parameter structure.

mapminmax('pdesc') returns the process parameter descriptions.

mapminmax('pcheck',FP) throws an error if any parameter is illegal.

Examples

Here is how to format a matrix so that the minimum and maximum values of each row are mapped to default interval [-1, 1].

*

x1 = [1 2 4; 1 1 1; 3 2 2; 0 0 0]

[y1,PS] = mapminmax(x1)

Next, apply the same processing settings to new values.

*

x2 = [5 2 3; 1 1 1; 6 7 3; 0 0 0]

y2 = mapminmax('apply',x2,PS)

Reverse the processing of y1 to get x1 again.

*

x1_again = mapminmax('reverse',y1,PS)

Algorithm

It is assumed that X has only finite real values, and that the elements of each row are not all equal.

*

y = (ymax-ymin)*(x-xmin)/(xmax-xmin) ymin;

如何把矩阵内的数据归一化处理

b=sqrt(sum(a.*a));

c = a./repmat(b,size(a,1),1);

c就是a经过矩阵

当然也可以写成一句

a = a./repmat(sqrt(sum(a.*a)),size(a,1),1);

matlab 怎样进行数据归一化

归一化P应

P1=[2056 2395 2600;

2395 2600 2298;

2600 2298 1634;

2298 1634 1600;

1634 1600 1873;

1600 1873 1478]‘;

取P1 中最大和最小元素分别为Pmax=2600，Pmin=1478，

则归一化后P的对应元素值为P=(P1-Pmin)/(Pmax-Pmin)。

origin中如何将数据归一化？

Analysis-Mathematics-Normalize。Done。

您好，我想问一下如何将一列数据，归一化到(0,2)？

可以将数据转换成数字，然后将数据映射到那个区间