Monthly Archives: July 2013

Programming instrumental music from scratch

July 29, 2013
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Programming instrumental music from scratch

I recently posted about automatically making music. The algorithm that I made pulled out interesting sequences of music from existing songs and remixed them. While this worked reasonably well, it also didn’t have full control over the basics of the music; it wasn’t actually specifying which instruments to use, or what notes to play. Maybe I’m being...

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Programming instrumental music from scratch

July 29, 2013
By
Programming instrumental music from scratch

I recently posted about automatically making music. The algorithm that I made pulled out interesting sequences of music from existing songs and remixed them. While this worked reasonably well, it also didn't have full control over the basics of the music; it wasn't actually specifying which instruments to use, or what notes to play. Maybe I'm being a control...

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Exploratory Data Analysis: Combining Histograms and Density Plots to Examine the Distribution of the Ozone Pollution Data from New York in R

Exploratory Data Analysis: Combining Histograms and Density Plots to Examine the Distribution of the Ozone Pollution Data from New York in R

Introduction This is a follow-up post to my recent introduction of histograms.  Previously, I presented the conceptual foundations of histograms and used a histogram to approximate the distribution of the “Ozone” data from the built-in data set “airquality” in R.  Today, I will examine this distribution in more detail by overlaying the histogram with parametric

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Easier Database Querying with R

July 29, 2013
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Easier Database Querying with R

I have a strong distaste for database connection management.  All I want to do when I want to query one of our many databases at work is to simply supply the query, and package the result into an R data.frame or data.table. R has many great database connection tools, including but not limited to RPostgreSQL,

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analyze the youth risk behavior surveillance system (yrbss) with r

July 29, 2013
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the youth risk behavior surveillance system is the high school edition of the behavioral risk factor surveillance system (brfss), a scientific study of good kids who do bad things.  questions are mostly about sex, drugs, rock and roll, and populat...

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BCEA 2.0

July 28, 2013
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BCEA 2.0

I know that updating a package too often is not quite good practice, so, given we've released BCEA 1.3-1 just about a month ago, this is way too soon to move forward. But between the last release and now, I've been doing some reading and have made some...

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Orthogonal Partial Least Squares (OPLS) in R

July 28, 2013
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Orthogonal Partial Least Squares (OPLS) in R

I often need to analyze and model very wide data (variables >>>samples), and because of this I gravitate to robust yet relatively simple methods. In my opinion partial least squares (PLS) is a particular useful algorithm. Simply put, PLS is an extension of principal components analysis (PCA), a non-supervised  method to maximizing  variance explained in X,

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Classification of the Hyper-Spectral and LiDAR Imagery using R (mostly). Part 1: Result Evaluation

July 28, 2013
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Classification of the Hyper-Spectral and LiDAR Imagery using R (mostly). Part 1: Result Evaluation

Introduction There was the EEEI Data Fusion Contest this spring. This year they wanted people to elaborate about hyper-spectral (142-bands imagery) and LiDAR data. The resolution of the data-set was about 5 m.  There were 2 nominations: best classification and  the best scientific paper.  I work with high-resolution imagery quite often, but classification is a very rear task for me...

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Hopfield Networks in Julia

July 28, 2013
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Hopfield Networks in Julia

As a fun side project last night, I decided to implement a basic package for working with Hopfield networks in Julia. Since I suspect many of the readers of this blog have never seen a Hopfield net before, let me explain what they are and what they can be used for. The short-and-skinny is that

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Modeling Match Results in La Liga Using a Hierarchical Bayesian Poisson Model: Part two.

July 28, 2013
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Modeling Match Results in La Liga Using a Hierarchical Bayesian Poisson Model: Part two.

In the last blog post I showed my initial attempt at modeling football results in La Liga using a Bayesian Poission model, but there was one glaring problem with the model; it did not consider the advantage of being the home team. In this post I will show how to fix this! I will also show a way...

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