Tag Archives: Numpy

Wind,Temperature,Air Pressure statistics

The above graphs show statistics from SMHI, the swedish meteorological and hydrographical institute, on air pressure, temperature and wind speed.  For each categrory, (pressure,temp,wind speed) there are two graphs: the first a frequency chart, the second a Q-Q-chart, showing the … Continue reading

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Wind Speed Distribution – is it Normal…?

As a sailor, I’ve become obsessed by wind. Trying to understand its behavior has kept me busy for decades, and still, I can’t claim I fully understand wind behavior. Anyways, one of the things I’ve wondered is whether wind is … Continue reading

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Map Projections using Python & Basemap

The numerous external libraries available for Python continue impressing me, in scope, numbers and ease of use.  A couple of hours ago I googled for chartographic tools for Python, and found the Basemap-library, an add-on to Matplotlib. For whatever reason, … Continue reading

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Machine Learning – results

So, I reached the point where my Neural Network , after a couple of thousand training iterations, is capable of correctly identifying and separating different categories of input into their corresponding classes. More specifically, as “targets”, I’ve chosen a number … Continue reading

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Machine Learning – normalizing your inputs

Often it pays off  to pre-process/normalize your input data before passing it further down the line of your Neural Network, eg. to get the range of input values into a scale more suitable for your network. Im experimenting with images, … Continue reading

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Neural Networks – training the network using an SGD

In order to understand how my chosen strategy for finding new weights, namely an SGD, actually operates, I did a bit of analysis. The graph below shows the flow of one single input, in a trivial, “linear” neural network, with only … Continue reading

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Signal vs Noise – target recognition using Neural Networks

A Python hack implementing a 3 level Neural Network. After 500 iterations of training, the network is capable of fully separating Targets (non random numeric patterns) from noise, i.e random numeric sequnces.

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Radar Plotting & Trial Manouver on iPad/iPhone

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