semvec/pypi-packages huggingface dataset:
distributions-bc 0.4
Gaussian and Binomial distributions
Requires Python: >=3.6

# distributions_bc package


## Prerequisites

Python 3.7.4+
matplotlib package


### Files

Contains 3 files:

* generaldistribution.py
* binomialdistribution.py
* gaussiandistribution.py


### Description

* generaldistribution.py contains a Distribution class



* binomialdistribution.py creates a Binomial class that inherits from the Distribution class. This Binomial class has attributes typically associated with a Binomial Distribution (n, p, k, mean, stdev etc.) that can be either specified or determined from a file read in. The Binomial class also has methods that allow the computation of the probability distribution function and its plotting. A magic method that allows the addition of 2 Binomial Objects with the same p is also present



* gaussiandistribution.py creates a Gaussian class that inherits from the Distribution class. This Gaussian class has attributes typically associated with a Gaussian Distribution (mean, stdev) that can be either specified or determined from a file read in. The Binomial class also has methods that allow the computation of the probability distribution function and its the plotting. A magic method that allows the addition of 2 Gaussian Objects is also present


### Installation

```bash
pip install distributions_bc
```

### Usage

```bash
from distributions_bc import Gaussian

g1 = Gaussian(mu = 23, sigma = 2)
g1.mean # 23
g1.stdev # 2
g1.pdf(x = 22) # 0.2787008074875851


g1.read_data_file('numbers.txt')  # Reads file and updates attributes and parameters
```



#### Authors

Charles Gauthey


#### License

distributions_bc is offered under the MIT license
wf-smcmodel-localize 0.0.1
Define a sequential Monte Carlo (SMC) model to estimate positions of objects from sensor data
Keywords: Bayes,SMC,localization

# smcmodel_localize

Define a sequential Monte Carlo (SMC) model to estimate positions of objects from sensor data

## Task list

* Make all data pipes work with Localizationodel or LocalizationModelMultilateration
* Make LocalizationModel and LocalizationModelMultilateration work directly with database connections (no data pipes)
* Remove all references to moving_objects in names; fixed objects are anchors
* Consistently call is measurement_value_name or measurement_value_field_name
* Fix up data source in smcmodel so it can be used more than once
* Make shoe sensor and tray sensor subclasses (or otherwise derivative of this module)
* Add functionality to migrate data from current CSV files to DatabaseConnection
* Make visualizations handle database connections directly
* Visualization: show date and time together on x axis
* Visualization: Add standard deviation to plots
* Visualization: Make functions more DRY by breaking out common code
* Replace print statements with logging
* Get better control over shallow vs. deep copies of dataframes
* Add ribbon for standard deviation to plotting functions?
* Add docstrings
* Generate documentation
wf-smcmodel 0.0.1
Provide estimation and simulation capabilities for sequential Monte Carlo (SMC) models
Keywords: Bayes,SMC

# smcmodel

Provide estimation and simulation capabilities for sequential Monte Carlo (SMC) models

## Task list

* Add logging
* Add docstrings
* Generate documentation
* Add explicit names to tensors and ops
* Check dataflow graphs using tensorboard (are all objects of known size at graph specification time)
* In simulate, do init and pull initial values in one run
* Figure out how to force assignment ops without fetching their outputs
* Consider adding num_samples argument to simulate()