statistics 1.0.14

Statistics package for easy and efficient data manipulation with built-in Bayesian Network (Bayes Net), many mathematical functions and tools.

statistics

Statistics package for easy and efficient data manipulation with built-in Bayesian Network (Bayes Net), many mathematical functions and tools.

API Documentation

See the API Documentation for a full list of functions, classes and extension.

Usage

Numeric extension:

import 'package:statistics/statistics.dart';

void main() {
  var ns = [10, 20.0, 30];
  print('ns: $ns');

  var mean = ns.mean;
  print('mean: $mean');

  var sdv = ns.standardDeviation;
  print('sdv: $sdv');

  var squares = ns.square;
  print('squares: $squares');
}

OUTPUT:

ns: [10, 20.0, 30]
mean: 20.0
sdv: 8.16496580927726
squares: [100.0, 400.0, 900.0]

Statistics

The class Statistics, that have many pre-computed statistics, can be generated from a numeric collection:

import 'package:statistics/statistics.dart';

void main() {
  var ns = [10, 20.0, 25, 30];
  var statistics = ns.statistics;

  print('Statistics.max: ${ statistics.max }');
  print('Statistics.min: ${ statistics.min }');
  print('Statistics.mean: ${ statistics.mean }');
  print('Statistics.standardDeviation: ${ statistics.standardDeviation }');
  print('Statistics.sum: ${ statistics.sum }');
  print('Statistics.center: ${ statistics.center }');
  print('Statistics.median: ${statistics.median} -> ${statistics.medianLow} , ${statistics.medianHigh}');
  print('Statistics.squaresSum: ${ statistics.squaresSum }');

  print('Statistics: $statistics');
}

OUTPUT:

Statistics.max: 30
Statistics.min: 10
Statistics.mean: 21.25
Statistics.standardDeviation: 22.5
Statistics.sum: 85.0
Statistics.center: 25
Statistics.median: 22.5 -> 20.0 , 25
Statistics.squaresSum: 2025.0
Statistics: {~21.25 +-22.5 [10..(25)..30] #4}

Bayesian Network

Bayesian Network, also known as Bayes Network, is a very important tool in understanding the dependency among events and assigning probabilities to them.

Here's an example of how to build a BayesianNetwork.

import 'package:statistics/statistics.dart';

void main(){
  var bayesNet = BayesianNetwork('cancer');

  // C (cancer) = T (true) ; F (false)
  bayesNet.addNode("C", [
    'F',
    'T',
  ], [], [
    "C = F: 0.99",
    "C = T: 0.01",
  ]);

  // X (exam) = P (positive) ; N (negative)
  bayesNet.addNode("X", [
    '+P',
    '-N',
  ], [
    "C"
  ], [
    "X = N, C = F: 0.91",
    "X = P, C = F: 0.09",
    "X = N, C = T: 0.10",
    "X = P, C = T: 0.90",
  ]);

  // Show the network nodes and probabilities:
  print(bayesNet);

  var analyser = bayesNet.analyser;

  // Ask the probability to have cancer with a positive exame (X = T): 
  var answer1 = analyser.ask('P(c|x)');
  print(answer1); // P(c|x) -> C = TRUE | X = P -> 0.09183673469387756

  // Ask the probability to have cancer with a negative exame (X = F):
  var answer2 = analyser.ask('P(c|-x)');
  print(answer2); // P(c|-x) -> C = T | X = N -> 0.0011086474501108647
}

Event Monitoring

To help to generate the probabilities you can use the EventMonitor class and then build the BayesianNetwork:

import 'package:statistics/statistics.dart';

void main() {
  // Monitor events to then build a Bayesian Network:
  // ** Note that this example is NOT USING REAL probabilities for Cancer!
  var eventMonitor = EventMonitor('cancer');

  // The prevalence of Cancer in the population:
  // - 1% (10:990):

  for (var i = 0; i < 990; ++i) {
    eventMonitor.notifyEvent(['CANCER=false']);
  }

  for (var i = 0; i < 10; ++i) {
    eventMonitor.notifyEvent(['CANCER=true']);
  }

  // The Exam performance when the person have cancer:
  // - 90% Sensitivity.
  // - 10% false negative (1:9).

  for (var i = 0; i < 9; ++i) {
    eventMonitor.notifyEvent(['EXAM=positive', 'CANCER=true']);
  }

  for (var i = 0; i < 1; ++i) {
    eventMonitor.notifyEvent(['EXAM=negative', 'CANCER=true']);
  }

  // The Exam performance when the person doesn't have cancer:
  // - 91% Specificity
  // - 9% false positive (89:901).

  for (var i = 0; i < 901; ++i) {
    eventMonitor.notifyEvent(['EXAM=negative', 'CANCER=false']);
  }
  for (var i = 0; i < 89; ++i) {
    eventMonitor.notifyEvent(['EXAM=positive', 'CANCER=false']);
  }

  var bayesNet = eventMonitor.buildBayesianNetwork();

  var analyser = bayesNet.analyser;

  var answer1 = analyser.ask('P(cancer)');
  print('- Cancer probability without an Exam:');
  print('  $answer1'); // P(cancer) -> CANCER = TRUE |  -> 0.01

  var answer2 = analyser.ask('P(cancer|exam)');
  print('- Cancer probability with a positive Exam:');
  print('  $answer2'); // P(cancer|exam) -> CANCER = TRUE | EXAM = POSITIVE -> 0.09183673469387756
}

See a full example for Bayes Net at GitHub:

CSV

To generate a CSV document, just use the extension generateCSV in your data collection. You can pass the parameter separator to change the value separator (default: ,).

import 'package:statistics/statistics.dart';

void main() {
  var categories = <String, List<double?>>{
    'a': [10.0, 20.0, null],
    'b': [100.0, 200.0, 300.0]
  };

  var csv = categories.generateCSV();
  print(csv);
}

OUTPUT:

#,a,b
1,10.0,100.0
2,20.0,200.0
3,0.0,300.0

Tools

Parsers:

• parseDouble
• parseInt
• parseNum
• parseDateTime
• All parses accepts a dynamic value as input and have a default value parameter def.

Formatters:

• formatDecimal
• DateTimeExtension.formatToYMD
• DateTimeExtension.formatToYMDHm
• DateTimeExtension.formatToYMDHms
• DateTimeExtension.formatToYMDHmZ
• DateTimeExtension.formatToYMDHmsZ

Extension:

• StringExtension.splitLines
• StringExtension.splitColumns
• StringExtension.containsAny
• IterableIterableExtension.toKeysMap
• IterableStringExtension.filterLines
• IterableStringExtension.toIntsList
• IterableStringExtension.toDoublesList
• IterableStringExtension.toNumsList

See the API Documentation for a full list of functions, extension and classes.

data_serializer

The statistics package exports the package data_serializer to help the handling of primitives, data, bytes and files.

• Some extension in data_serializer were originally in the statistics package.

Test Coverage

This package aims to always have a high test coverage percentage, over 95%. With that the package can be a reliable tool to support your important projects.

Source

The official source code is hosted @ GitHub:

• https://github.com/gmpassos/statistics

Features and bugs

Please file feature requests and bugs at the issue tracker.

Contribution

Any help from the open-source community is always welcome and needed:

• Found an issue?
  • Please fill a bug report with details.
  • Wish a feature?
    • Open a feature request with use cases.
  • Are you using and liking the project?
    • Promote the project: create an article, do a post or make a donation.
  • Are you a developer?
    • Fix a bug and send a pull request.
    • Implement a new feature.
    • Improve the Unit Tests.
  • Have you already helped in any way?
    • Many thanks from me, the contributors and everybody that uses this project!

If you donate 1 hour of your time, you can contribute a lot, because others will do the same, just be part and start with your 1 hour.

Author

Graciliano M. Passos: gmpassos@GitHub.

License

Apache License - Version 2.0

See Also

Take a look at SciDart, an experimental cross-platform scientific library for Dart by Angelo Polotto.