1. Gaussian 09w Package Software Package
2. Gaussian 09w
3. Gaussian 09 Manual
4. Gaussian 09 Basis Sets
5. Gaussian 16
6. Gaussian 09w Linux

The Gaussian 09 license prohibits the publication of comparative benchmark data. A SLURM script must be used in order to run a Gaussian09 job. HPCC main cluster Penzias supports Gaussian jobs demanding up to 64GB of memory.

In some cases, Gaussian output will display the references relevant to the current calculation type. Gaussian also includes the NBO program as link 607. If this program is used, it should be cited separately as: NBO Version 3.1, E. Carpenter, and F. Gaussian is a computational chemistry program available to students, staff and faculty. See accessing the software share for more information. Installation Instructions. In the Gaussian folder on cheme-software, select the most recent edition of Gaussian (currently Gaussian 09 Rev D.01) and open that folder.Then open the folder Gaussian 09 folders and, in there, select the. Gaussian 09W (G09) is a computational chemistry program that runs on any mod-ern Windows 32-bit PC. If you want to install G09 on a 64bit PC, there is a special procedure you must follow: 1.Insert the CD with G09 and copy its content onto you computer. Any folder will do; I copied directly into the:Cndirectory. 2.Open directory containing G09. General overview of my workflow when using G09. This can be used a tutorial and example files can be found at: https://github.com/gclen/gaussianfiles.

Gaussian Naive Bayes (GaussianNB)

Can perform online updates to model parameters via partial_fit.For details on algorithm used to update feature means and variance online,see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

Read more in the User Guide.

Prior probabilities of the classes. If specified the priors are notadjusted according to the data.

Portion of the largest variance of all features that is added tovariances for calculation stability.

number of training samples observed in each class.

probability of each class.

class labels known to the classifier

absolute additive value to variances

variance of each feature per class

mean of each feature per class

Examples

Methods

Fit Gaussian Naive Bayes according to X, y

Training vectors, where n_samples is the number of samplesand n_features is the number of features.

Target values.

Weights applied to individual samples (1. for unweighted).

New in version 0.17: Gaussian Naive Bayes supports fitting with sample_weight.

Get parameters for this estimator.

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Parameter names mapped to their values.

Incremental fit on a batch of samples.

This method is expected to be called several times consecutivelyon different chunks of a dataset so as to implement out-of-coreor online learning.

This is especially useful when the whole dataset is too big to fit inmemory at once.

This method has some performance and numerical stability overhead,hence it is better to call partial_fit on chunks of data that areas large as possible (as long as fitting in the memory budget) tohide the overhead.

Training vectors, where n_samples is the number of samples andn_features is the number of features.

Target values.

List of all the classes that can possibly appear in the y vector.

Must be provided at the first call to partial_fit, can be omittedin subsequent calls.

Weights applied to individual samples (1. for unweighted).

Perform classification on an array of test vectors X.

Gaussian 09w Package Software Package

Predicted target values for X

Gaussian 09w

Return log-probability estimates for the test vector X.

Returns the log-probability of the samples for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attribute classes_.

Return probability estimates for the test vector X.

Returns the probability of the samples for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attribute classes_.

Return the mean accuracy on the given test data and labels.

Gaussian 09 Manual

In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach label set be correctly predicted.

Test samples.

True labels for X.

Sample weights.

Mean accuracy of self.predict(X) wrt. y.

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such as Pipeline). The latter haveparameters of the form <component>__<parameter> so that it’spossible to update each component of a nested object.

Estimator parameters.

Gaussian 09 Basis Sets

Estimator instance.

Gaussian 16

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Gaussian 09w Linux