biclustering | Parallel Biclustering Algorithm - Fast algorithm

by KronicDeth Python Version: Current License: GPL-2.0

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | biclustering Summary

biclustering is a Python library. biclustering has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Parallel Biclustering Algorithm - Fast algorithm for finding all biclusters in a Gene Expression Matrix (GEM)

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biclustering has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

biclustering has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of biclustering is current.

Quality

biclustering has 0 bugs and 0 code smells.

Security

biclustering has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

biclustering code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

biclustering is licensed under the GPL-2.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

biclustering releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

It has 1379 lines of code, 144 functions and 11 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed biclustering and discovered the below as its top functions. This is intended to give you an instant insight into biclustering implemented functionality, and help decide if they suit your requirements.

Calculate the chain length
Calculate the progress bar
Log the progress bar
Pool the cache
Find all biclusters
Set the index of the biclusters
Chain biclusters
Count the number of bicluster clusters
Perform duplicate search
Return the number of rows
Refreshes the binning
Returns a numpy array where values are in the given order
Pack data into a flat array
Return an numpy array size
Return a human - readable summary report
Refreshes the bounding box
Return elements where value is a value

Get all kandi verified functions for this library.

biclustering Key Features

No Key Features are available at this moment for biclustering.

biclustering Examples and Code Snippets

No Code Snippets are available at this moment for biclustering.

Community Discussions

Trending Discussions on biclustering

scikit-learn spectral clustering: unable to find NaN lurking in data

Statsmodels: requires arrays without NaN or Infs - but test shows there are no NaNs or Infs

ValueError: array must not contain infs or NaNs in SpectralCoclustering in Python3.X

Parallel DBSCAN in ELKI

QUESTION

scikit-learn spectral clustering: unable to find NaN lurking in data

Asked 2018-Nov-21 at 06:37

I'm running spectral coclustering on this dataset of Jeopardy questions, and there is this frustrating issue I'm facing with the data. Note that I'm only clustering all the values in the 'question' column.

There is apparently a "divide by zero" ValueError occurring when I run biclustering on the dataset.

...

ANSWER

Answered 2018-Nov-19 at 01:13

Some strings sequence like e.g. 'down out' results in a zero return value from TfidfVectorizer(). That causes the errors starting with a divide by zero error, which results in inf values in the mtx sparse matrix and this causes the second error.

As a workaround to this problem to remove this sequences or remove the zero matrix elements from the mtx matrix after it created by TfidfVectorizer.fit_transform(), which a bit tricky because of the sparse matrix operation.

I made the second solution, as I didn't dived into the original tasks, as follows:

Source https://stackoverflow.com/questions/53358270

QUESTION

Statsmodels: requires arrays without NaN or Infs - but test shows there are no NaNs or Infs

Asked 2018-Aug-21 at 16:49

I am trying to run an ADF-test from the statsmodels' adfuller module. It gives me an error:

...

ANSWER

Answered 2018-Apr-06 at 01:27

I have solved it now via:

Source https://stackoverflow.com/questions/49682633

QUESTION

ValueError: array must not contain infs or NaNs in SpectralCoclustering in Python3.X

Asked 2018-Feb-20 at 19:34

data = np.genfromtxt("breastCancer.txt", delimiter=',').astype(np.float32)
data = data[~np.isnan(data).any(axis=1)]

ROW, COLUMN = data.shape

label = data[:, -1]
input = data[:, 1:COLUMN - 1]

scaler = preprocessing.MinMaxScaler(feature_range=(-1.0, 1.0))
scaler.fit(input)
input = scaler.transform(input)

model = SpectralCoClustering(n_clusters=3, random_state=0)
model.fit(input)

...

ANSWER

Answered 2018-Feb-20 at 19:34

I spent two days figuring out the same problem. My solution: before doing model.fit(input) I removed columns with only zeros from input:

Source https://stackoverflow.com/questions/45198876

QUESTION

Parallel DBSCAN in ELKI

Asked 2018-Jan-24 at 14:47

Here I can see that there exists class clustering.gdbscan.parallel.ParallelGeneralizedDBSCAN, but when I tried to invoke it, I've got error:

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ANSWER

Answered 2018-Jan-24 at 12:32

The parallel DBSCAN version is not in the 0.7.1 release, but you need to compile it yourself.

It currently does not include progress logging, and it is a rather naive parallelization. It works okay if the majority of time is spent in neighbor search, because the cluster labeling is synchronized. (But if all your cores are loaded, synchronization should be fine).

I just pushed a change that adds progress logging to Parallel GDBSCAN.

Make sure to add an index. For most data sets, indexes yield considerable speedups. With indexes, the rather poor parallelization of this implementation will surface, and you see more and more threads waiting for synchronization.

Source https://stackoverflow.com/questions/48383925

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install biclustering

You can download it from GitHub.
You can use biclustering 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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