FunctionGenerateRandomPointsInShapeLibrary "FunctionGenerateRandomPointsInShape"
Generate random vector points in geometric shape (parallelogram, triangle)
random_parallelogram(vector_a, vector_b) Generate random vector point in a parallelogram shape.
Parameters:
vector_a : float array, vector of (x, y) shape.
vector_b : float array, vector of (x, y) shape.
Returns: float array, vector of (x, y) shape.
random_triangle(vector_a, vector_b) Generate random vector point in a triangle shape.
Parameters:
vector_a : float array, vector of (x, y) shape.
vector_b : float array, vector of (x, y) shape.
Returns: float array, vector of (x, y) shape.
Random
FunctionProbabilityDistributionSamplingLibrary "FunctionProbabilityDistributionSampling"
Methods for probability distribution sampling selection.
sample(probabilities) Computes a random selected index from a probability distribution.
Parameters:
probabilities : float array, probabilities of sample.
Returns: int.
FunctionSMCMCLibrary "FunctionSMCMC"
Methods to implement Markov Chain Monte Carlo Simulation (MCMC)
markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
target_path : float array, target path array.
position : int, index of the path.
last_value : float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
start_value : float, base value to start simulation.
n_iterations : integer, number of iterations to run.
Returns: float array with path.
FunctionDecisionTreeLibrary "FunctionDecisionTree"
Method to generate decision tree based on weights.
decision_tree(weights, depth) Method to generate decision tree based on weights.
Parameters:
weights : float array, weights for decision consideration.
depth : int, depth of the tree.
Returns: int array
