FetchBuddle {Buddle} R Documentation

## Predicting Classification and Regression.

### Description

Yield prediction (softmax value or value) for regression and classification for given data based on the results of training.

### Usage

```FetchBuddle(X, lW, lb, lParam)
```

### Arguments

 `X` a matrix of real values which will be used for predicting classification or regression. `lW` a list of weight matrices obtained after training. `lb` a list of bias vectors obtained after training. `lParam` a list of parameters used for training. It includes: label, hiddenlayer, batch, drop, drop.ratio, lr, init.weight, activation, optim, type, rand.eff, distr, and disp.

### Value

A list of the following values:

predicted

predicted real values (regression) or softmax values (classification).

One.Hot.Encoding

one-hot encoding values of the predicted softmax values for classification. For regression, a zero matrix will be returned. To convert the one-hot encoding values to labels, use OneHot2Label().

### References

 Geron, A. Hand-On Machine Learning with Scikit-Learn and TensorFlow. Sebastopol: O'Reilly, 2017. Print.

 Han, J., Pei, J, Kamber, M. Data Mining: Concepts and Techniques. New York: Elsevier, 2011. Print.

 Weilman, S. Deep Learning from Scratch. O'Reilly Media, 2019. Print.

### See Also

CheckNonNumeric(), GetPrecision(), MakeConfusionMatrix(), OneHot2Label(), Split2TrainTest(), TrainBuddle()

### Examples

```
### Using mnist data again

data(mnist_data)

X1 = mnist_data\$Images       ### X1: 100 x 784 matrix
Y1 = mnist_data\$Labels       ### Y1: 100 x 1 vector

############################# Train Buddle

lst = TrainBuddle(Y1, X1, train.ratio=0.6, arrange=TRUE, batch.size=10, total.iter = 100,
hiddenlayer=c(20, 10), batch.norm=TRUE, drop=TRUE,
drop.ratio=0.1, lr=0.1, init.weight=0.1,
activation=c("Relu","SoftPlus"), optim="AdaGrad",
type = "Classification", rand.eff=TRUE, distr = "Logistic", disp=TRUE)

lW = lst[]
lb = lst[]
lParam = lst[]

X2 = matrix(rnorm(20*784,0,1), 20,784)  ## Genderate a 20-by-784 matrix

lst = FetchBuddle(X2, lW, lb, lParam)   ## Pass X2 to FetchBuddle for prediction

```

[Package Buddle version 2.0.1 Index]