koljamaier’s gists

koljamaier / Multi-output model only evaluating as having one output column.

Created October 20, 2018 21:46 — forked from C4N4D4M4N/Multi-output model only evaluating as having one output column.

	package com.IceKontroI.MODEL_TEST;

	import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
	import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
	import org.deeplearning4j.nn.conf.inputs.InputType;
	import org.deeplearning4j.nn.conf.layers.DenseLayer;
	import org.deeplearning4j.nn.conf.layers.OutputLayer;
	import org.deeplearning4j.nn.graph.ComputationGraph;
	import org.nd4j.linalg.api.buffer.DataBuffer;
	import org.nd4j.linalg.api.ndarray.INDArray;

koljamaier / NqueensTest.scala

Created June 29, 2018 22:04

This is a simple Test suite for scala unit testing

	package nqueens

	import org.junit.runner.RunWith
	import org.scalatest.FunSuite
	import org.scalatest.junit.JUnitRunner

	@RunWith(classOf[JUnitRunner])
	class NqueensTest extends FunSuite {

	// test("string take") {

koljamaier / Nqueen.scala

Created June 29, 2018 22:03

Small example from Oderskys "Scala By Example" Book extended by a customized filter-approach. Find the test-file in another Gist

	package nqueens

	object Nqueens extends App {

	println((nQueens(100) map show) mkString "\n")

	def isValid(partialSolution: List[Int], col: Int, n: Int): Boolean = {
	val partialSolutionCoords = (0 until partialSolution.length) zip partialSolution.reverse
	val partial = partialSolutionCoords.toSet

koljamaier / JsonParsing.scala

Created June 10, 2018 12:04

This snippet show the usage of jacksons fasterxml library in scala for my use case

	package recfun.week3

	import com.fasterxml.jackson.databind.JsonNode
	import scala.collection.JavaConversions._
	import com.fasterxml.jackson.module.scala.DefaultScalaModule
	import com.fasterxml.jackson.databind.ObjectMapper

	import scala.util.parsing.json.JSON

	object JsonParsing {

koljamaier / moving_average_vis.py

Created July 25, 2017 10:06

Visualization of a moving average

	from __future__ import division
	import pylab
	import numpy as np
	"""
	This script visualizes a trend in linear-like-shaped data
	generated with random noise.
	"""


	# generate linear like function (with noise)

koljamaier / dataframe.py

Created October 28, 2016 07:32

Spark DataFrame example

	from pyspark import SparkContext, SparkConf
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	import json
	import re

	api_pattern = re.compile(r'(POST\|HEAD\|GET).HTTP......[0-9].[0-9]*')

	def matcher( str ):
	match = api_pattern.search(str)

koljamaier / texprocessing.py

Created October 28, 2016 07:25

Spark cluster text processing vs. single node

	From this little example created by myself I learned, that it is always important to think in the distributed MapReduce paradigm when getting hands on spark.
	Operations that are costly calculations in a centralized manner should be avoided on the cluster.

koljamaier / titanicNaiveBayes.py

Last active October 28, 2016 06:56

Naive Bayes in the Kaggle Titanic Competition

	from __future__ import division
	import numpy as np
	from matplotlib import pyplot as plt
	from scipy.stats import norm
	import pandas as pd
	import csv as csv
	import seaborn as sns
	from numpy import matrix, mat
	import re

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