#### Nick Koutras

##### Member

based on ideas by Dennis Bassboss and Beaker, and implemented by

Nick Koutras.

Purpose: To select and display n numbers that will constitute the

prediction for the next draw of a Lotto game based on the

below described algorithm.

Description

Accepted Inputs:

Start Draw

Duration

Backward draws to include as predictors

Quantity of predicted numbers.

Trials

Step 1. Train the neural network

The backward draws are analyzed and the top x numbers with frequency

of appearance >1 are used. The quantity of x varies according to

draws specified. Always the top numbers of the frequency are used.

Following a FIFO (first in first out) priority queue, we collect these

results for up to 80% of the draws specified by Duration.

Step 2. Formulate the equations

From the data collected we design the neural network to be used

as predictor.

Step 3. Test the NN

We test the predictive ability of the net vs the remaining 20% of the draws

using the above neural network.

Step 4. Display statistical results.

The collected data from step 3 are displayed on the screen complete

with the ability factor of the neural net, R² value.

Step 5. Display the n numbers that the network will select

as predictions for the next draw.

Step 5 will be repeated as many times as Trials indicate.

Please note that is backward test algorithm. That means that we can test

our predictions on previous draws based on the above algorithm.

First trial will be ready this week.

If you have any recommentations please let me know.

Nick