Library "FunctionNNLayer" Generalized Neural Network Layer method. function(inputs, weights, n_nodes, activation_function, bias, alpha, scale) Generalized Layer. Parameters: inputs : float array, input values. weights : float array, weight values. n_nodes : int, number of nodes in layer. activation_function : string, default='sigmoid',...

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Library "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron. Parameters: value : float, value to process. Returns: float linear(value) Input is the same as output. Parameters: value : float, value to process. Returns: float sigmoid(value) ...

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Library "MLLossFunctions" Methods for Loss functions. mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ". Parameters: expects : float array, expected values. predicts : float array, prediction values. Returns: float binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log). Parameters: ...

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Library "FunctionNNPerceptron" Perceptron Function for Neural networks. function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks. Parameters: inputs : float array, the inputs of the perceptron. weights : float array, the weights for inputs. bias : float, default=1.0, the default bias...

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Hello Traders/Programmers, For long time I thought that if it's possible to make a script that has own memory and criterias in Pine. it would learn and find patterns as images according to given criterias. after we have arrays of strings, lines, labels I tried and made this experimental script. The script works only for Long positions. Now lets look at how it...

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This script is the 2nd version of the BTC Deep Learning (ANN) system. Created with the following indicators and tools: RSI MACD MOM Bollinger Bands Guppy Exponential Moving Averages: (3,5,8,10,12,15,30,35,40,45,50,60) Note: I was inspired by the CM Guppy Ema script. Thank you very much to dear wroclai for his great help. He has been a big help in the deep...

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This script created by training WTI 4 hour data , 7 indicators and 12 Guppy Exponential Moving Averages. Details : Learning cycles: 1 AutoSave cycles: 100 Training error: 0.007593 ( Smaller than average target ! ) Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 300 Validating example rows: 0 Querying example rows: 0 Excluded...

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This script is formed by training the S & P 500 Index with various indicators. Details : Learning cycles: 78089 AutoSave cycles: 100 Training error: 0.011650 (Far less than the target, but acceptable.) Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 300 Validating example rows: 0 Querying example rows: 0 Excluded...

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