mapminmax

(To be removed) Process matrices by mapping row minimum and maximum values to [-1 1]

mapminmax will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

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

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

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

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

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

Description

Tip

To rescale data for deep learning workflows, use the Normalization name value pair for the input layer.

[Y,PS] = mapminmax(X,YMIN,YMAX) takes a N-by-Q matrix, X and optionally a minimum and a maximum value for each row of Y, YMIN and YMAX, and returns a N-by-Q matrix, Y, and a process settings that allow consistent processing of values, PS.

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

[Y,PS] = mapminmax(X,FP) takes parameters as a struct: FP.ymin, FP.ymax.

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

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

dx_dy = mapminmax('dx_dy',X,Y,PS) returns the reverse derivative.

Examples

Format a Matrix With the mapminmax Function

This example shows 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)

Input Arguments

X — Input matrix

matrix

Matrix you want to process, specified as an N-by-Q matrix.

YMIN — Minimum value

-1 (default) | scalar

Minimum value for each row of the output matrix Y, specified as a scalar.

YMAX — Maximum value

1 (default) | scalar

Maximum value for each row of the output matrix Y, specified as a scalar.

Output Arguments

Y — Output matrix

matrix

Processed matrix, returned as an N-by-Q matrix.

PS — Process settings

structure

Process settings that allow consistent processing of values, returned as a structure.

More About

Normalize Inputs and Targets Using mapminmax

Before training, it is often useful to scale the inputs and targets so that they always fall within a specified range. The function mapminmax scales inputs and targets so that they fall in the range [–1,1]. The following code illustrates how to use this function.

[pn,ps] = mapminmax(p);
[tn,ts] = mapminmax(t);
net = train(net,pn,tn);

The original network inputs and targets are given in the matrices p and t. The normalized inputs and targets pn and tn that are returned will all fall in the interval [–1,1]. The structures ps and ts contain the settings, in this case the minimum and maximum values of the original inputs and targets. After the network has been trained, the ps settings should be used to transform any future inputs that are applied to the network. They effectively become a part of the network, just like the network weights and biases.

If mapminmax is used to scale the targets, then the output of the network will be trained to produce outputs in the range [–1,1]. To convert these outputs back into the same units that were used for the original targets, use the settings ts. The following code simulates the network that was trained in the previous code, and then converts the network output back into the original units.

an = sim(net,pn);
a = mapminmax('reverse',an,ts);

The network output an corresponds to the normalized targets tn. The unnormalized network output a is in the same units as the original targets t.

If mapminmax is used to preprocess the training set data, then whenever the trained network is used with new inputs they should be preprocessed with the minimum and maximums that were computed for the training set stored in the settings ps. The following code applies a new set of inputs to the network already trained.

pnewn = mapminmax('apply',pnew,ps);
anewn = sim(net,pnewn);
anew = mapminmax('reverse',anewn,ts);

For most networks, including feedforwardnet, these steps are done automatically, so that you only need to use the sim command.

Algorithms

It is assumed that X has only finite real values, and that the elements of each row are not all equal. (If xmax=xmin or if either xmax or xmin are non-finite, then y=x and no change occurs.)

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

Version History

Introduced in R2006a

R2026a: mapminmax will be removed

mapminmax will be removed in a future release.

To learn more about data processing for neural networks, see these examples and topics:

• Preprocess Data for Domain-Specific Deep Learning Applications

• Datastores for Deep Learning

• Choose an App to Label Ground Truth Data

To standardize data using the fitrnet (Statistics and Machine Learning Toolbox) and fitcnet (Statistics and Machine Learning Toolbox) functions, use the Standardize (Statistics and Machine Learning Toolbox) name-value argument. To map the input data of a dlnetwork object for tabular data to the range [-1, 1], use a featureInputLayer and set the Normalization argument to "rescale-symmetric".

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.