static void Main(string[] args)
{
//XORTest();
FileInfo f = new FileInfo(@"C:\Users\Wes\Downloads\Bitfinex_BTCUSD_1h.csv");
List <MarketData> test = f.ReadCSV <MarketData>();
int[] inputs = new int[] { 1, 30, 50, 50, 30, 6 };
var net = new FFNeuralNetwork(inputs);
//Data still needs normalized but basic principle is here
foreach (var data in test)
{
net.FeedForward(data.FloatDate);
net.BackProp(data.ToArray());
}
var now = new float[] { DateTime.Now.AddHours(1).Ticks };
var results = net.FeedForward(now);
foreach (var r in results)
{
Console.WriteLine(r);
}
}
//public sealed class MarketMap : ClassMap<MarketData>
//{
// public MarketMap()
// {
// var mm = Map(x => x.Date);
// }
//}
private static void XORTest()
{
int iterations = 50000;
int[] layers = new int[] { 4, 10, 25, 10, 1 };
FFNeuralNetwork nn = new FFNeuralNetwork(layers);
for (int i = 0; i < iterations; i++)
{
#region 4-XOR
nn.FeedForward(new float[] { 0, 0, 0, 1 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 0, 0, 1, 0 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 0, 0, 1, 1 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 0, 1, 0, 0 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 0, 1, 0, 1 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 0, 1, 1, 0 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 0, 1, 1, 1 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 1, 0, 0, 0 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 1, 0, 0, 1 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 1, 0, 1, 0 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 1, 0, 1, 1 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 1, 1, 0, 0 });
nn.BackProp(new float[] { 0 });
nn.FeedForward(new float[] { 1, 1, 0, 1 });
nn.BackProp(new float[] { 1 });
nn.FeedForward(new float[] { 1, 1, 1, 0 });
nn.BackProp(new float[] { 1 });
//nn.FeedForward(new float[] { 1, 1, 1, 1 });
//nn.BackProp(new float[] { 0 });
if (i % 1000 == 0)
{
Console.Clear();
//Console.WriteLine(nn.ToString());
var results = new List <float>()
{
nn.FeedForward(new float[] { 0, 0, 0, 1 })[0],
nn.FeedForward(new float[] { 0, 0, 1, 0 })[0],
nn.FeedForward(new float[] { 0, 0, 1, 1 })[0],
nn.FeedForward(new float[] { 0, 1, 0, 0 })[0],
nn.FeedForward(new float[] { 0, 1, 0, 1 })[0],
nn.FeedForward(new float[] { 0, 1, 1, 0 })[0],
nn.FeedForward(new float[] { 0, 1, 1, 1 })[0],
nn.FeedForward(new float[] { 1, 0, 0, 0 })[0],
nn.FeedForward(new float[] { 1, 0, 0, 1 })[0],
nn.FeedForward(new float[] { 1, 0, 1, 0 })[0],
nn.FeedForward(new float[] { 1, 0, 1, 1 })[0],
nn.FeedForward(new float[] { 1, 1, 0, 0 })[0],
nn.FeedForward(new float[] { 1, 1, 0, 1 })[0],
nn.FeedForward(new float[] { 1, 1, 1, 0 })[0],
nn.FeedForward(new float[] { 1, 1, 1, 1 })[0],
};
foreach (var Z in results)
{
Console.WriteLine(Z);
}
}
#endregion
}
var result = nn.FeedForward(new float[] { 1, 1, 1, 1 });
Console.WriteLine(result[0]);
}
