I'm new to Lightroom and I'm trying to edit a timelapse. I've got several shots and i want that from the first one to the last one the exposure, noise reduction and other filters, increase in a linear way, is that possible?
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In neural networks, in general, which model should yield a better and accurate output between both for time series?
As you rightly mentioned, We can use linear regression with time series data as long as:
The inclusion of lagged terms as regressors does not create a collinearity problem.
Both the regressors and the explained variable are stationary.
Your errors are not correlated with each other.
The other linear regression assumptions apply.
No autocorrelation is the single most important assumption in linear regression. If autocorrelation is present the consequences are the following:
Bias: Your “best fit line” will likely be way off because it will be pulled away from the “true line” by the effect of the lagged errors.
Inconsistency: Given the above, your sample estimators are unlikely to converge to the population parameters.
Inefficiency: While it is theoretically possible, your residuals are unlikely to be homoskedastic if they are autocorrelated. Thus, your confidence intervals and your hypothesis tests will be unreliable.
While, The Long Short Term Memory neural network is a type of a Recurrent Neural Network (RNN). RNNs use previous time events to inform the later ones. For example, to classify what kind of event is happening in a movie, the model needs to use information about previous events. RNNs work well if the problem requires only recent information to perform the present task. If the problem requires long term dependencies, RNN would struggle to model it. The LSTM was designed to learn long term dependencies. It remembers the information for long periods.
To focus on the 1st sequence. The model takes the feature of the time bar at index 0 and it tries to predict the target of the time bar at index 1. Then it takes the feature of the time bar at index 1 and it tries to predict the target of the time bar at index 2, etc. The feature of 2nd sequence is shifted by 1 time bar from the feature of 1st sequence, the feature of 3rd sequence is shifted by 1 time bar from 2nd sequence, etc. With this procedure, we get many shorter sequences that are shifted by a single time bar.
I'm developing machine learning algorithms which classify images based on training data.
During the image preprocessing stages, there are several parameters which I can modify that affect the data I feed my algorithms (for example, I can change the Hessian Threshold when extracting SURF features). So the flow thus far looks like:
[param1, param2, param3...] => [black box] => accuracy %
My problem is: with so many parameters at my disposal, how can I systematically pick values which give me optimized results/accuracy? A naive approach is to run i nested for-loops (assuming i parameters) and just iterate through all parameter combinations, but if it takes 5 minute to calculate an accuracy from my "black box" system this would take a long, long time.
This being said, are there any algorithms or techniques which can search for optimal parameters in a black box system? I was thinking of taking a course in Discrete Optimization but I'm not sure if that would be the best use of my time.
Thank you for your time and help!
Edit (to answer comments):
I have 5-8 parameters. Each parameter has its own range. One parameter can be 0-1000 (integer), while another can be 0 to 1 (real number). Nothing is stopping me from multithreading the black box evaluation.
Also, there are some parts of the black box that have some randomness to them. For example, one stage is using k-means clustering. Each black box evaluation, the cluster centers may change. I run k-means several times to (hopefully) avoid local optima. In addition, I evaluate the black box multiple times and find the median accuracy in order to further mitigate randomness and outliers.
As a partial solution, a grid search of moderate resolution and range can be recursively repeated in the areas where the n-parameters result in the optimal values.
Each n-dimensioned result from each step would be used as a starting point for the next iteration.
The key is that for each iteration the resolution in absolute terms is kept constant (i.e. keep the iteration period constant) but the range decreased so as to reduce the pitch/granular step size.
I'd call it a ‘contracting mesh’ :)
Keep in mind that while it avoids full brute-force complexity it only reaches exhaustive resolution in the final iteration (this is what defines the final iteration).
Also that the outlined process is only exhaustive on a subset of the points that may or may not include the global minimum - i.e. it could result in a local minima.
(You can always chase your tail though by offsetting the initial grid by some sub-initial-resolution amount and compare results...)
Have fun!
Here is the solution to your problem.
A method behind it is described in this paper.
I have some data that tells me the amount of hours water is available for particular towns.
You can see it here
I want to use train a Multilayer Perceptron based on that data, to take a set of coordinates and indicate the approximate number of hours for which that coordinate will have water.
Does this make sense?
If so, am I correct in saying, there has to be two input layers? One for lat and one for long. And the output layer should be the number of hours.
Would love some guidance.
I would solve that differently:
Just create an ArrayList of WaterInfo:
WaterInfo contains lat,lon, waterHours.
Then for a given coordinate search the closest WaterInfo in the list.
Since you have not many elements, just do a brute force search, to find the closest.
You further can optimize, to find the three closest WaterInfo points, and calculate the weithted average of WaterHours. As weight you use the air distance from current position to Waterinfo position.
To answer your question:
"Does this makes sense"?
From the goal to get a working solution: NO!
Ask yourself, why do you want to use MLP for this task.
Further i doubt that using two layers for lat / long makes sense.
A coordinate (lat/lon) is one point on the world, so that should be one layer in the model. You can convert the lat/lon coord to a cell identifier: Span a grid over Brazil; with cell width 10 or 50km; now convert a lat/long coordinate to a cellId: Like E4 on a chess board, you will calculate one integer value representing the cell. (There are other solutions to get an unique number, too, choose one you like)
Now you have a modell geoCellID -> waterHours, which better represents the real world situation.
I'm trying to minimize a function using one of the scipy minimizers. Unfortunately my function has plateaus of equal value so minimisers get stuck there. I was wondering which of the scipy optimisers would be least sensitive to this and why?
I know I could start a number of times at random locations but I'm not able to do that with what I am currently working on and have to use on of these minimisers out of the box.
Add a linear function of the coordinates to your function to give some nonzero, but very small slope to the flat areas. If your minimum/maximum is in a flat area, you need to decide which part of the flat area to choose as your final answer, so you might as well bias the whole search. After this arrives at a minimum/maximum, rerun the optimization using that as the starting point and no added bias.
If there is a way to determine the boundaries of the search space, then you might try a large number of starting locations that uniformly sample the search space.
I am working on a program that needs to fit numerous cosine waves in order to determine one of the parameters for the function. The equation that I am using is y = y_0 + Acos((4*pi*L)/x + pi) where L is the value that I am trying to obtain from the best fit line.
I know that it is possible to do this correctly by hand for each set of data, but what is the best way to automate this process? I am currently reading in the data from text files, and running a loop with the initial paramiters changing until I have an array of paramater values that have an amplitude similar to the data, then I check the percent difference between points on the center peak and two end peaks to try to pick the best one. It in consistently picking lower values than what I get when fitting by hand (almost exactly one phase off). So is there a way to improve this method, or another method that works better?
Edit: My LabVIEW version has a cos fitting VI which is what I am using, the problem is when I try to automate the fitting by changing the initial parameters using a loop, I cant figure out how to get the program to pick the same best fit line as a human would pick.
Why not just use a Fast Fourier Transform? This should be way faster than fitting a cosine. In the result vector of complex numbers look for the largest peak of in the totals. You're given frequency (position in the FFT result vector), amplitude and phase.
You can evaluate the goodness of the fit by computing the difference between fitting curve and your data. A VI does this in the "Advanced curve fitting" palette. Then all you have to do is pick up the best fit.