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Some months ago, I started a new job as a machine learning engineer for a german trading company. In the course of this, I began to deal more with the programming language Scala. Scala provides some benefits that makes the work easier for data engineers when building data pipelines and there's a good support for spark, which is an advantage in cluster computing. But Scala (of course) also has its idiosyncrasies. This article shows the idiosyncrasy of Yate's correction in the Statistcs package of Scala and its consequences. The Statistics Package Like many other programming languages, Scala also comes with a statistical package called "Statistics". It includes many basic methods of the describing statistics, which you need for many purposes. One of these purposes is, for example, A/B testing. So, what's wrong with Scala? For an A/B test, I tested the significance by own calculations first (in old manner, as I had learned in my studies) before using th

For several months I have been an enthusiastic owner of a Sonos home sound system. In addition to a brilliant sound and the operation via smartphone and WLAN, this also offers the possibility to implement a control via the SOAP protocol to implement user-defined functions. This means, that it's possible to "hack" the Sonos devices. Free APIs Available Fortunately, some non-proprietary APIs are available on the net or on GitHub: For Java on any machine and OS (by VMichalak) For python on Raspberry Pi SoCo API for Python on Raspberry Pi It has to be said, that the Python APIs not neccessarily run on Windows (at least they didn't run on my machine), but this is not to expect when they are proclaimed as a Raspberry Pi project. Dependency Nightmare Though the API of VMichalak was the one that worked for me, it has a lot of dependecies which need to be downloaded or to be downloaded an build what requires Maven. OKHTTP (okhttp-3.11.0.jar) OKIO (okio-1

This is the R script used to create and train the neuronal net for the Python project . Please consider that the lines for the export of the neuron weights are disabled by the comment marker #. ################################## # # COMMON SAMPLE DATA HANDLING # ################################## load_samples = function (filename, attrib) { data = read.csv(filename, sep = "\t" ) # Remove result column data = data[, -5 ] # Calculate all combination with count of measure points n = aggregate(x ~ run + subset, data = data, FUN = length) # Keep only measurement with 80 points n = n[n[, 3 ] == 80 ,] # Create the training data matrix for non-matches dataset = matrix(ncol = 80 , nrow = 0 ) # Fill up the training data matrix with the valid cases (n) of data and transpose it for (i in 1 : dim(n)[ 1 ]) dataset = rbind(dataset, t(data $ x[(data $ run == n[i, 1 ]) & (data $ subset == n[i, 2 ])])) # add class column dataset = cbind(c(attr

This source code is part of the Python magnetic field sensor and detector project . This class only provides a simple flashing indicator light for the project. ######################################## # # Python Magnetic Field Logger and Detector # by Sebastian Folz # Introduced on Pi And More 11 # ######################################## # Don't use time module, because there is no valid time without WiFi import time import sqlite3 as lite from qmc5883 import QMC5883 import statistics # import functions for system calls from subprocess import call import sys # import pushbutton class from pushbutton import pushbutton, Push # import blink class from Blinker import Blink1, Blink2 from enum import Enum import RPi.GPIO as GPIO # fix copy bug with switched arguments from shutil import copyfile # Import own neural net class from MLP import MLNN TASTER = 38 LED = 40 LONG_CYCLES = 10 # 1.5 seconds COMMIT = 10 BINSIZE = 10 #

As on PAM 11 in Trier introduced: Python-Logger-Class (Push Button Library) as part of the Python magnetic field logger and detector project . ######################################## # # Python Magnetic Field Logger and Detector # by Sebastian Folz # Pusbutton library - handles push buttons # Introduced on Pi And More 11 # ######################################## # import GPIO import RPi.GPIO as GPIO # enumerations from enum import Enum class Push (Enum): NONE = 0 SHORT_DOWN = 1 LONG_DOWN = 2 class pushbutton : #pushed = False def __init__ ( self ): GPIO . setmode(GPIO . BOARD) self . pushed = False self . pin = - 1 self . cycles = 0 def setLongCycle ( self , cycles): self . long_cycles = cycles; def configure ( self , aPin): self . pin = aPin GPIO . setup( self . pin, GPIO . IN, pull_up_down = GPIO . PUD_UP) def isPushed ( self ): #

As introduces atn PAM 11 in Trier: The Python-Logger-Class. This is the main class for my Raspberry Pi 3 machine learning experiment. Please consider, that three more classes are required: The QMC5883 driver class , the pushbutton class and the blinker class . Furthermore, the R script for the training of the neuronal net is required. ######################################## # # Python Magnetic Field Logger and Detector # by Sebastian Folz # Introduced on Pi And More 11 # ######################################## # Don't use time module, because there is no valid time without WiFi import time import sqlite3 as lite from qmc5883 import QMC5883 import statistics # import functions for system calls from subprocess import call import sys # import pushbutton class from pushbutton import pushbutton, Push # import blink class from Blinker import Blink1, Blink2 from enum import Enum import RPi.GPIO as GPIO # fix copy bug with switched argume