discretize | Discretization tools for finite volume and inverse problems
kandi X-RAY | discretize Summary
kandi X-RAY | discretize Summary
Discretization tools for finite volume and inverse problems.
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- Refine a set of octaves
- Parse a location type
- Reshape a mesh
- Convert a tensor into a tensor
- The stencil cell gradient
- R Calculates the Dx cell gradient
- Validate the boundary conditions
- Gradient of cell gradient
- R Compute the diagonal of a vector
- R Return the Pix matrix of a mesh
- Return the average edge_z_to_cell
- Plot a 3D mesh
- Generate a random model
- R Conretize a vector
- Return average edge y to cell center
- Calculate the average face z to cell coordinates
- Return the average face y to cell coordinates
- Returns average face coordinates in cell coordinates
- R Return the projection of a bounding box
- Calculate the difference between faces and volumes
- Calculate the difference between the faces and volumes
- The cell gradient of the cell
- Compute the interpolation matrix for a given location
- Compute the facez divergence of the mesh
- The gradient of the stencil cell gradient
- Returns the average edge coordinates
discretize Key Features
discretize Examples and Code Snippets
Lines of Code : 231License : Non-SPDX (Apache License 2.0)
Copydef auc(labels,
predictions,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
curve='ROC',
name=None,
summation_method='trapezoidal',
thresho Community Discussions
Trending Discussions on discretize
QUESTION
Below you will find my python code for a class assignment I was given a couple weeks ago which I have been unable to successfully debug. The problem is about finding the value at risk (i.e., the p% quantile) for an aggregate loss random variable, using FFT. We are given a clear mathematical procedure by which we can gain an estimation of the discretized CDF of the aggregate loss random variable. My results are, however, seriously off and I am making some kind of mistake which I have been unable to find even after hours of debugging my code.
The aggregate loss random variable S is given such that S=sum(X_i for i in range(N)), where N is negative binomially distributed with r=5, beta=.2, and X_i is exponentially distributed with theta=1. The probability generating function for this parametrization is P(z)=[1-\beta(z-1)]^{-r}.
We were asked to approximate the distribution of S by
- choosing a grid width
hand an integernsuch thatr=2^nis the number of elements to discretizeXon, - discretizing
Xand calculating the probabilities of being in equally spaced intervals of widthh, - applying the FFT to the discretized
X, - applying the PGF of
Nto the elements of the Fourier-transformedX, - applying the inverse FFT to this vector.
The resulting vector should be an approximation for the probability masses of each such interval for S. I know from previous methods that the 95% VaR ought to be ~4 and the 99.9% VaR ought to be ~10. But my code returns nonsensical results. Generally speaking, my index where the ECDF reaches levels >0.95 is way too late, and even after hours of debugging I have not managed to find where I am going wrong.
I have also asked this question on the math stackexchange, since this question is very much on the intersection of programming and math and I have no idea at this moment whether the issue is on the implementation side of things or whether I am applying the mathematical ideas wrong.
...ANSWER
Answered 2022-Apr-03 at 14:31Not sure about math, but in snippet variable r gets overrided, and when computing f_tilde_vec_fft function PGF uses not 5 as expected for r, but 1024. Fix -- change name r to r_nb in definition of hyperparameters:
r_nb, beta, theta = 5, .2, 1
and also in function PGF:
return (1 - beta * (z - 1)) ** (-r_nb)
After run with other parameters remain same (such as h, n etc.) for VaRs I get [4.05, 9.06]
QUESTION
The dataframe I am talking about is this
I am interested in only a subset of the products and I want to transform the data so instead of having "item" columns I have columns with the names of the products I am interested in with values of 0 or 1 indicating whether or not the said product is in the basket. What I have done so far is this
...ANSWER
Answered 2022-Mar-26 at 15:55Use:
QUESTION
I am trying to integrate an expression that has real and complex values defining it as a lambda expression. The integration variable is kx and the resulting solution of the integral will be evaluated in x and y dimensions, but after I integrate and try to evaluate the integral I get the following error:
...ANSWER
Answered 2022-Mar-02 at 20:52A few things
- You forgot a comma in the last lambda
- your lambda has three arguments, quad integrates over the first argument, you have to pass the other arguments with
args=(x,y). The limits of integration in your example are-100*kto+100*k. - there were some
^where**was expected. - The quad returns a tupple with integral value and integral error, so you are interested in the first element of the output, you can get it with the
[0]
QUESTION
I need some help/suggestions/guidance on how I can optimize my code. The code works, but with huge data it has been running for almost a day. My data has ~ 2 million rows , with sample data ( few thousdand rows) it works .My sample data format is show below:
...ANSWER
Answered 2022-Feb-03 at 09:38You were on the right track! pd.cut is the way to go. I'm using the Series categories to create your final bins:
QUESTION
I have to discretize into at least 5 bins a continuous target variable in order to lower the complexity of a classification model using the sklearn library
In order to do this, I've used the KBinsDiscretizer but I don't know how can I split in balanced parts the dataset now that I've discretized the target variable. This is my code:
...ANSWER
Answered 2022-Jan-23 at 20:35Your y_train and y_test are parts of y, which has (it seems) the original continuous values. So you're ending up fitting multiclass classification models, with probably lots of different classes, which likely causes the crashes.
I assume what you wanted is
QUESTION
I wanted a python alternative to discretize2d in R. An alternative I found over stackoverflow was to use pandas.crosstab and pandas.cut as so,
...ANSWER
Answered 2022-Jan-09 at 10:55I think you are looking for numpy.histogram2d:
Example:
QUESTION
I am writing a code IN Python to compute the discrete Laplacian as a sparse matrix in 2D. The code is as follows:
...ANSWER
Answered 2021-Dec-29 at 16:24The slow performance comes from the bad complexity of the algorithm. Indeed, the complexity of the original code is O(N**4)! This is due to np.concatenate which creates a new array by copying the old one and adding a few items at the end of the new one. This means that O(N**2) copies of 3 growing arrays are performed. In general, you should avoid np.concatenate in a loop to make a growing array. You should use Python lists in that case.
Note that you can use np.tile to repeat values of an array and pre-compute the constant dxx * np.array([-4, 1, 1, 1, 1]).
Here is the corrected code:
QUESTION
I have a continuous input function which I would like to discretize into lets say 5-10 discrete bins between 1 and 0. Right now I am using np.digitize and rescale the output bins to 0-1. Now the problem is that sometime datasets (blue line) yield results like this:
I tried pushing up the number of discretization bins but I ended up keeping the same noise and getting just more increments. As an example where the algorithm worked with the same settings but another dataset:
this is the code I used there NumOfDisc = number of bins
ANSWER
Answered 2021-Dec-13 at 14:22If what I described in the comments is the problem, there are a few options to deal with this:
- Do nothing: Depending on the reason you're discretizing, you might want the discrete values to reflect the continuous values accurately
- Change the bins: you could shift the bins or change the number of bins, such that relatively 'flat' parts of the blue line stay within one bin, thus giving a flat green line in these parts as well, which would be visually more pleasing like in your second plot.
QUESTION
x=rand(1,10); bins=discretize(x,0:0.25:1);
An instance of running the above line in Matlab R2020b produces the following outputs for x and bins.
...ANSWER
Answered 2021-Dec-09 at 12:51You can use interp1 with 'previous' option:
QUESTION
I have a dataset that contains a column of datetime of a month, and I need to divide it into two blocks (day and night or am\pm) and then discretize the time in each block into 10mins bins. I could add another column of 0 and 1 to show it is am or pm, but I cannot discretize it! Can you please help me with it?
...ANSWER
Answered 2021-Nov-21 at 22:47If I understood correctly you are trying to add a column for every interval of ten minutes to indicate if an observation is from that interval of time.
You can use lambda expressions to loop through each observation from the series.
Dividing by 10 and making this an integer gives the first digit of the minutes, based on which you can add indicator columns.
I also included how to extract the day indicator column with a lambda expression for you to compare. It achieves the same as your np.where().
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Install discretize
You can use discretize like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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