Library "FunctionMinkowskiDistance" Method for Minkowski Distance, The Minkowski distance or Minkowski metric is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance. It is named after the German mathematician Hermann Minkowski. reference:...

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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 "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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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 "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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This script plots vertical lines on charts or indicators. Unfortunately pinescript is lacking a vertical line plotting function. Vertical lines are useful to mark events, such as crossover of levels, indicators signals or as a time marker. After searching the internet for a long time and trying different scripts, this script is the simplest and visually the...

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Calculates weighted mean, variance, standard deviation, MSE and RMSE from time series variables or arrays. When calculating from arrays, the function expects index 0 to be the most recent sample and weight values.

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This indicator provides some base code for looping over data to identify swings in price action. Full code commentary can be found on the backtest rookies website. The indicator shall allow users to "analyse" a recent historical candle to detect whether it was a swing point. This will work by inputting a number to select which historical candle you want to...

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Description: A function that returns a polynomial regression and deviation information for a data set. Inputs: _X: Array containing x data points. _Y: Array containing y data points. Outputs: _predictions: Array with adjusted _Y values. _max_dev: Max deviation from the mean. _min_dev: Min deviation from the mean. ...

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Hi fellow traders.. Happy to share a Linear Regression & RSI Multi-Function Custom Screener with Table-Labels... The Screener scans for Linear Regression 2-SD Breakouts and RSI OB/OS levels for the coded tickers and gives Summary alerts Uses Tables (dynamica resizing) for the scanner output instead of standard labels! This Screener cum indicator collection has...

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EXPERIMENTAL: Using keltner channels with automatic multiplier finding, offsets and show_last cutoffs to generate a forecast area. video showing why its named keltner worms :p.. streamable.com

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function to get sorted indices from a array using bubble sort.

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Trying different solutions to find the minimum/maximum value in a set of predefined series

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Example function of a markov chain monte carlo simulation.

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An utility function to parse session inputs. Extracts hours, minutes and weekdays (if defined) and returns a tuple as the result. _parseSession(sessionString) => (hourStart, minuteStart, hourEnd, minuteEnd, weekdaysArray) Examples presented on the chart.

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Description: A Function that returns a linear regression channel using (X,Y) vector points. Inputs: _X: Array containing x data points.¹ _Y: Array containing y data points.¹ Note: ¹: _X and _Y size must match. Outputs: _predictions: Array with adjusted _Y values at _X. _max_dev: Max deviation from the mean. _min_dev:...

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