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how to deploy sensor nodes randomly in a matrix 100x100
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https://www.mathworks.com/matlabcentral/fileexchange/28770-introduction-to-classification?focused=6789585 . When you download the .zip then itside it there is a .xlsx file containing white wine data.
Your hypothesis is that the cloudiness of Trockenbeerenauslese in any given year is correlated to the thunderstorms two years afterwards?
Then what are the hypotheses?
In order to use naive Bayes you need "events" to measure the occurrence of and then use to calculate conditional probability. A list of temperatures is not an event. Whether there is rain or not is a potential event, but there is no inherent nonzero amount of rain that is an "event" separate from some other nonzero amount of rain. "Not a cloud in the sky" is a potential event. "Partly cloudy" is a poor event, since it applies whenever there is even just one lone cloud at 10 km up. "Partly cloudy" in the sense of the sun being blocked or reduced from time to time needs to be considered in conjunction with the angle to the Sun, since a cloud way off on the horizon that blocks the setting sun might have to do with a weather system that cannot affect you.
Overcast? Another poor event. High thin clouds of ice crystal can cause overcast and that is a very very different weather situation than low heavy clouds that would burst into rain seemingly if a bird flew through them and farted.
A list of pressures — those are not events either.
Now, "temperature exceeds 32.8 degrees", that is an event. So is temperature is between 32.8 and 33.1. So is temperature is between 31.2 and 32.9. Notice the overlap:
- boundaries on conditions may be arbitrary
- boundaries may overlap
- events do not need to be independent to be considered as events.
The arbitrary nature of boundaries for events means that YOU have to decide what boundaries to use. You have to create hypotheses, such as that such and such a range of temperatures is significant. At the time you frame the hypotheses you do not need to have any evidence yet that the range truly is significant: that is what your naive Bayes calculation will tell you.
It is valid to make hypotheses at different scales on the same data, such as binning at 0.2 degrees and also binning at 5 degrees. This would permit you to find effects in groups and zoom down to find the most important subgroups, and it would also allow you to look at a weak effect and zoom out to larger intervals to see if the effect holds (you were looking too narrowly) or perhaps to discover that at larger scale it just washes out, suggesting that it might have been statistically insignificant.
The more events you partition out of the same data, the longer your computation takes, being proportional to the number of events per attribute, all multiplied together.b
But naive Bayes itself is not concerned with how you determine the events: it just needs the multidimensional histogram of the statistics.
There are no inherent events in that white wine data. But just like with the weather examples I gave earlier,
"Now, "temperature exceeds 32.8 degrees", that is an event. So is temperature is between 32.8 and 33.1. So is temperature is between 31.2 and 32.9. "
Likewise you can create events such as "pH between 2.3 and 2.8", "pH between 2.75 and 2.79", "Citric Acid < 0.35". But which events to create is something best done by someone with a subject knowledge.
I know that I have no idea at what point the Sulphates start being detectable in the taste, and no idea what factors might act to mask the sulphate flavor. But perhaps you do not care about sulphates -- talking about sulphates might not be part of any of the hypotheses you are interested in. Perhaps you want to know how chlorides relate to density, or how "quality" and "Free S02" together predict VolAcid.
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