# User Guide" In FisPro: Fuzzy Inference System Design and Optimization

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>"
)
```

## Introduction

This package is the R implementation of functions to manage a Fuzzy Inference System (FIS) provided by the open source software FisPro.
FisPro allows to create Fuzzy Inference Systems and to use them for reasoning purposes, especially for simulating a physical or biological system.
In this brief User Guide we describe how to build and use a FIS to infer input values.
See Fuzzy Logic Elementary Glossary for more details about Fuzzy Logic.

```library(FisPro)
```

## Build a FIS from a configuration file

The FIS configuration file can be designed using the FisPro open source software.

```fis_file <- system.file("extdata", "test.fis", package = "FisPro")
fis <- NewFis(fis_file)
```

## Build a FIS from scratch

Create a new empty FIS.
The design must be completed using the available functions to add inputs, outputs and rules before it can be used for inference.

```fis <- NewFis()
fis\$name <- "foo"
```

### Create inputs

Add 2 inputs to the FIS.

Create the first input with 2 MFs regular standardized fuzzy partition:

```fisin1 <- NewFisIn(2, 0, 1)
fisin1\$name <- "input1"
```

Create the second input with 3 MFs:

```fisin2 <- NewFisIn(0, 1)
fisin2\$name <- "input2"

mf1 <- NewMfTrapezoidalInf(0, 0.5)
mf1\$label <- "Low"

mf2 <- NewMfTriangular(0, 0.5, 1)
mf2\$label <- "Average"

mf3 <- NewMfTrapezoidalSup(0.5, 1)
mf3\$label <- "High"

```

### Create outputs

Add 2 outputs to the FIS.

Create a crisp output with range [0, 1]:

```fisout1 <- NewFisOutCrisp(0, 1)
fisout1\$name <- "output1"
```

Create a fuzzy output with 2 MFs regular standardized fuzzy partition in range [0, 1]:

```fisout2 <- NewFisOutFuzzy(2, 0, 1)
fisout2\$name <- "output2"
```

### Create the rule base

Add 2 rules to the FIS.
Each rule is initialized with a vector of premises and conclusions.
- a premise is the 1-based index of MF in the input [FisIn], 0 means the rule is incompelete.
- a conclusion is a numeric value for crisp output [FisOutCrisp], or the 1-based index of MF in the fuzzy output [FisOutFuzzy].
In this example the second rule is incomplete, the second input of the FIS has no effect on this rule.

```fis\$add_rule(NewRule(c(1, 2), c(0, 1)))
```

### Save the FIS configuration file

Save the FIS to the file "foo.fis":

```fis\$save("foo.fis")
```
```file.remove("foo.fis")
```

## FIS inference

Infers all outputs:

```inferred <- fis\$infer(c(0.25, 0.75))
```

Infers first output:

```inferred_output1 <- fis\$infer_output(c(0.25, 0.75), 1)
```

Infers second output:

```inferred_output2 <- fis\$infer_output(c(0.25, 0.75), 2)
```

Infers dataset:

```test_file <- system.file("extdata", "test_data.csv", package = "FisPro")