Enzo Reyes myrddian

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myrddian / mock_pandas.py

Created September 15, 2019 13:59

	#Mock Classes that simulate a Pandas frame

	class loc_class:
	def __init__(self):
	self.rows = []
	self.csv_df = None

	def add_row(self, item):
	self.rows.append(item)

myrddian / my_bayes.R

Last active July 29, 2019 04:11

	library(mvtnorm)
	library(ggplot2)
	library(MCMCpack)

	expected_task <- list(mean=3,sd=2)
	hyper_parameters <- list(alpha=1, beta=1)
	data_samples = rnorm(100,6,3)
	random_samples <- rnorm(100,20,5)

	find_likelyhood <- function(target_model, target) {

myrddian / map_find.R

Last active July 19, 2019 04:53

	library(MCMCpack)

	example_prior <- list(mean=3,sd=2)
	hyper_parameters <- list(alpha=1, beta=1)
	data_samples = rnorm(100,6,3)
	random_samples <- rnorm(100,20,5)

	find_likelyhood <- function(target_model, target) {
	return(dnorm(target,target_model$mean, target_model$sd))
	}

myrddian / excel2csv.py

Last active November 18, 2018 11:34

	excel_file = 'excel.xlsx'
	out_file = 'excel.csv'

	#External Global variables
	__col_size__ = 14
	__i_start__ = 6
	__c_size__ = 202
	"""Read the country file and parse its contents"""
	def read_country_data(xls_file, country_size, index_start, output_file, col_size):
	print('Conversion Starting')

myrddian / xml2json.py

Created November 18, 2018 11:28

Example conversion of XML to JSON

	import json
	import io
	from lxml import etree

	def read_xml(file_name):
	in_file = open(file_name,"rb")
	xml_file = etree.parse(in_file)
	return xml_file

	def generate_rterms_dic(xml_element):

myrddian / random_dirichlet .R

Last active September 29, 2018 06:05

Generates a random dirichlet with n-attributes

	#returns a randomly generated dirichlet for psuedo counts
	#Alpha is passed as Shape to the RGamma Function
	#Beta is passed as scale to the RGamma Function
	#NVar is the number of variables you are going to psuedocount
	random_dirichlet <- function(n_var, alpha=0, beta=1) {

	return_val <- 0

	#Prevent alpha being less than n_var other wise
	#n_var will have empty psuedo-counts, which is what

myrddian / A2.R

Created September 29, 2018 04:38

	#returns a randomly generated dirichlet
	random_dirichlet <- function(n_var, alpha, beta) {

	return_val <- 0
	for(i in 1:n_var) {
	return_val[i] <- rgamma(1,shape=alpha,scale = beta)
	}

	#Normalise and round to a nice values
	return_val <- round(return_val/sum(return_val) * 100,1)/100

myrddian / optimiser.R

Created September 28, 2018 07:06

	model.analysis <-function(test_res, predict_rsp) {


	col_names <- c("TruePositive","FalsePositive",
	"FalseNegative","Accuracy","Precision","Recall","F1")
	ret_val <- data.frame(matrix(ncol = length(col_names), nrow = 0))
	colnames(ret_val) <- col_names
	ret_val[nrow(ret_val) + 1,] = c(tp,fp,fn,accuracy,precision,recall,f1)
	return(ret_val)
	}