### Bug
Fix: Fixed deeptree algorithm mismatched prediction due to re-ordered
index. Re-install updated version from CRAN /Github.

## deepdive

**deepnet->deeptree->deepforest**

This package aims to provide simple intuitive functions to create
quick prototypes of artificial neural network or deep learning models
for general purpose application. In addition, check out experimental
algorithms from my personal research , **deeptree** and
**deepforest** for special cases to achieve better accuracy
/ generalization.

**deeptree**: This algorithm builds a CART tree to
divide the solution space in to leaves and fits an artificial neural
network to each leaf. This approach takes advantage of distinct
properties of a tree and neural network. It has tendency to overfit but
multiple parameters can be tuned to achieve better generalization. This
model has a provision to stack predictions from other models(currently
stacking is only available for regression).

**deepforest**: This algorithm builds multiple
deepnets/deeptrees from which either best deepnet can be selected by
passing all variable and data to each network or random
deepnets/deeptrees based on random cuts of variable/data can be combined
together over a error choice.

*Reach me Out* :
rajeshbalakrishnan24@gmail.com
for any suggestions and doubts or you can always leave a comment on
github.

## Installation

You can install released version from CRAN or development from github
deepdive from GitHub
with:

```
#CRAN
install.packages("deepdive")
#Development Version
devtools::install_github("RajeshB24/deepdive")
```

## Example

This is a basic example which shows you how to solve a common
problem:

```
library(deepdive)
x <- data.frame(a = runif(1000)*100,
b = runif(1000)*200,
c = runif(1000)*100
)
y<- data.frame(y=20*x$a +30* x$b+10*x$c +10)
#Training increase iterations for convergence
modelnet<-deepnet(x,y,c(2,2),
activation = c('relu',"sin"),
reluLeak = 0.001,
modelType = "regress",
iterations =20,
eta=0.8,
optimiser="adam")
```

```
## iteration 3: 3601.9636637046
## iteration 7: 2740.03667239216
## iteration 11: 2028.79900757686
## iteration 15: 2089.61649378778
## iteration 20: 2191.53218537219
```

```
#predict
# predDeepNet<-predict.deepnet(modelnet,newData=x)
#evaluate
#sqrt(mean((predDeepNet$pred_y-y$y)^2))
```