# logp

Log unconditional probability density for discriminant analysis classifier

## Syntax

```lp = logp(obj,Xnew) ```

## Description

`lp = logp(obj,Xnew)` returns the log of the unconditional probability density of each row of `Xnew`, computed using the discriminant analysis model `obj`.

## Input Arguments

 `obj` Discriminant analysis classifier, produced using `fitcdiscr`. `Xnew` Matrix where each row represents an observation, and each column represents a predictor. The number of columns in `Xnew` must equal the number of predictors in `obj`.

## Output Arguments

 `lp` Column vector with the same number of rows as `Xnew`. Each entry is the logarithm of the unconditional probability density of the corresponding row of `Xnew`.

## Examples

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Construct a discriminant analysis classifier for Fisher's iris data, and examine its prediction for an average measurement.

Load Fisher's iris data and construct a default discriminant analysis classifier.

```load fisheriris Mdl = fitcdiscr(meas,species);```

Find the log probability of the discriminant model applied to an average iris.

`logpAverage = logp(Mdl,mean(meas))`
```logpAverage = -1.7254 ```