Updates to the documentation (linguistic corrections) (#414)

* Fix typo in Features list

* Update distance.md documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation

* Fix grammatical mistakes in documentation
This commit is contained in:
Attila Bakos 2019-11-02 10:41:34 +00:00 committed by Arkadiusz Kondas
parent f30e576c70
commit 7d5c6b15a4
27 changed files with 82 additions and 83 deletions

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@ -15,7 +15,7 @@ $associator = new Apriori($support = 0.5, $confidence = 0.5);
### Train ### Train
To train a associator simply provide train samples and labels (as `array`). Example: To train an associator, simply provide train samples and labels (as `array`). Example:
``` ```
$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']]; $samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
@ -31,7 +31,7 @@ You can train the associator using multiple data sets, predictions will be based
### Predict ### Predict
To predict sample label use `predict` method. You can provide one sample or array of samples: To predict sample label use the `predict` method. You can provide one sample or array of samples:
``` ```
$associator->predict(['alpha','theta']); $associator->predict(['alpha','theta']);
@ -43,7 +43,7 @@ $associator->predict([['alpha','epsilon'],['beta','theta']]);
### Associating ### Associating
Get generated association rules simply use `rules` method. To get generated association rules, simply use the `rules` method.
``` ```
$associator->getRules(); $associator->getRules();
@ -52,7 +52,7 @@ $associator->getRules();
### Frequent item sets ### Frequent item sets
Generating k-length frequent item sets simply use `apriori` method. To generate k-length frequent item sets, simply use the `apriori` method.
``` ```
$associator->apriori(); $associator->apriori();

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@ -14,7 +14,7 @@ $classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));
## Train ## Train
To train a classifier simply provide train samples and labels (as `array`). Example: To train a classifier, simply provide train samples and labels (as `array`). Example:
``` ```
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]]; $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
@ -28,7 +28,7 @@ You can train the classifier using multiple data sets, predictions will be based
## Predict ## Predict
To predict sample label use `predict` method. You can provide one sample or array of samples: To predict sample label use the `predict` method. You can provide one sample or array of samples:
``` ```
$classifier->predict([3, 2]); $classifier->predict([3, 2]);

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@ -4,7 +4,7 @@ Classifier based on applying Bayes' theorem with strong (naive) independence ass
### Train ### Train
To train a classifier simply provide train samples and labels (as `array`). Example: To train a classifier, simply provide train samples and labels (as `array`). Example:
``` ```
$samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]]; $samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
@ -18,7 +18,7 @@ You can train the classifier using multiple data sets, predictions will be based
### Predict ### Predict
To predict sample label use `predict` method. You can provide one sample or array of samples: To predict sample label use the `predict` method. You can provide one sample or array of samples:
``` ```
$classifier->predict([3, 1, 1]); $classifier->predict([3, 1, 1]);

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@ -21,7 +21,7 @@ $classifier = new SVC(Kernel::RBF, $cost = 1000, $degree = 3, $gamma = 6);
### Train ### Train
To train a classifier simply provide train samples and labels (as `array`). Example: To train a classifier, simply provide train samples and labels (as `array`). Example:
``` ```
use Phpml\Classification\SVC; use Phpml\Classification\SVC;
@ -38,7 +38,7 @@ You can train the classifier using multiple data sets, predictions will be based
### Predict ### Predict
To predict sample label use `predict` method. You can provide one sample or array of samples: To predict sample label use the `predict` method. You can provide one sample or array of samples:
``` ```
$classifier->predict([3, 2]); $classifier->predict([3, 2]);
@ -74,7 +74,7 @@ $classifier = new SVC(
$classifier->train($samples, $labels); $classifier->train($samples, $labels);
``` ```
Then use `predictProbability` method instead of `predict`: Then use the `predictProbability` method instead of `predict`:
``` ```
$classifier->predictProbability([3, 2]); $classifier->predictProbability([3, 2]);

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@ -16,7 +16,7 @@ $dbscan = new DBSCAN($epsilon = 2, $minSamples = 3, new Minkowski($lambda=4));
### Clustering ### Clustering
To divide the samples into clusters simply use `cluster` method. It's return the `array` of clusters with samples inside. To divide the samples into clusters, simply use the `cluster` method. It returns the `array` of clusters with samples inside.
``` ```
$samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]]; $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];

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@ -15,11 +15,11 @@ $kmeans = new KMeans(4, KMeans::INIT_RANDOM);
### Clustering ### Clustering
To divide the samples into clusters simply use `cluster` method. It's return the `array` of clusters with samples inside. To divide the samples into clusters, simply use the `cluster` method. It returns the `array` of clusters with samples inside.
``` ```
$samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]]; $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
Or if you need to keep your indentifiers along with yours samples you can use array keys as labels. Or if you need to keep your identifiers along with yours samples you can use array keys as labels.
$samples = [ 'Label1' => [1, 1], 'Label2' => [8, 7], 'Label3' => [1, 2]]; $samples = [ 'Label1' => [1, 1], 'Label2' => [8, 7], 'Label3' => [1, 2]];
$kmeans = new KMeans(2); $kmeans = new KMeans(2);
@ -32,8 +32,8 @@ $kmeans->cluster($samples);
#### kmeans++ (default) #### kmeans++ (default)
K-means++ method selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. K-means++ method selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.
It use the DASV seeding method consists of finding good initial centroids for the clusters. It uses the DASV seeding method consists of finding good initial centroids for the clusters.
#### random #### random
Random initialization method chooses completely random centroid. It get the space boundaries to avoid placing clusters centroid too far from samples data. Random initialization method chooses completely random centroid. It gets the space boundaries to avoid placing cluster centroids too far from samples data.

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@ -1,6 +1,6 @@
# Random Split # Random Split
One of the simplest methods from Cross-validation is implemented as `RandomSpilt` class. Samples are split to two groups: train group and test group. You can adjust number of samples in each group. One of the simplest methods from Cross-validation is implemented as `RandomSpilt` class. Samples are split to two groups: train group and test group. You can adjust the number of samples in each group.
### Constructor Parameters ### Constructor Parameters
@ -14,7 +14,7 @@ $randomSplit = new RandomSplit($dataset, 0.2);
### Samples and labels groups ### Samples and labels groups
To get samples or labels from test and train group you can use getters: To get samples or labels from test and train group, you can use getters:
``` ```
$dataset = new RandomSplit($dataset, 0.3, 1234); $dataset = new RandomSplit($dataset, 0.3, 1234);

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@ -1,8 +1,8 @@
# Stratified Random Split # Stratified Random Split
Analogously to `RandomSpilt` class samples are split to two groups: train group and test group. Analogously to `RandomSpilt` class, samples are split to two groups: train group and test group.
Distribution of samples takes into account their targets and trying to divide them equally. Distribution of samples takes into account their targets and trying to divide them equally.
You can adjust number of samples in each group. You can adjust the number of samples in each group.
### Constructor Parameters ### Constructor Parameters
@ -16,7 +16,7 @@ $split = new StratifiedRandomSplit($dataset, 0.2);
### Samples and labels groups ### Samples and labels groups
To get samples or labels from test and train group you can use getters: To get samples or labels from test and train group, you can use getters:
``` ```
$dataset = new StratifiedRandomSplit($dataset, 0.3, 1234); $dataset = new StratifiedRandomSplit($dataset, 0.3, 1234);
@ -41,4 +41,4 @@ $dataset = new ArrayDataset(
$split = new StratifiedRandomSplit($dataset, 0.5); $split = new StratifiedRandomSplit($dataset, 0.5);
``` ```
Split will have equals amount of each target. Two of the target `a` and two of `b`. Split will have equal amounts of each target. Two of the target `a` and two of `b`.

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@ -2,7 +2,7 @@
Helper class that holds data as PHP `array` type. Implements the `Dataset` interface which is used heavily in other classes. Helper class that holds data as PHP `array` type. Implements the `Dataset` interface which is used heavily in other classes.
### Constructors Parameters ### Constructor Parameters
* $samples - (array) of samples * $samples - (array) of samples
* $labels - (array) of labels * $labels - (array) of labels
@ -15,7 +15,7 @@ $dataset = new ArrayDataset([[1, 1], [2, 1], [3, 2], [4, 1]], ['a', 'a', 'b', 'b
### Samples and labels ### Samples and labels
To get samples or labels you can use getters: To get samples or labels, you can use getters:
``` ```
$dataset->getSamples(); $dataset->getSamples();
@ -24,7 +24,7 @@ $dataset->getTargets();
### Remove columns ### Remove columns
You can remove columns by index numbers, for example: You can remove columns by their index numbers, for example:
``` ```
use Phpml\Dataset\ArrayDataset; use Phpml\Dataset\ArrayDataset;

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@ -2,11 +2,11 @@
Helper class that loads data from CSV file. It extends the `ArrayDataset`. Helper class that loads data from CSV file. It extends the `ArrayDataset`.
### Constructors Parameters ### Constructor Parameters
* $filepath - (string) path to `.csv` file * $filepath - (string) path to `.csv` file
* $features - (int) number of columns that are features (starts from first column), last column must be a label * $features - (int) number of columns that are features (starts from first column), last column must be a label
* $headingRow - (bool) define is file have a heading row (if `true` then first row will be ignored) * $headingRow - (bool) define if the file has a heading row (if `true` then first row will be ignored)
``` ```
$dataset = new CsvDataset('dataset.csv', 2, true); $dataset = new CsvDataset('dataset.csv', 2, true);

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@ -2,7 +2,7 @@
Helper class that loads dataset from files. Use folder names as targets. It extends the `ArrayDataset`. Helper class that loads dataset from files. Use folder names as targets. It extends the `ArrayDataset`.
### Constructors Parameters ### Constructor Parameters
* $rootPath - (string) path to root folder that contains files dataset * $rootPath - (string) path to root folder that contains files dataset

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@ -1,6 +1,6 @@
# MnistDataset # MnistDataset
Helper class that load data from MNIST dataset: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/) Helper class that loads data from MNIST dataset: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)
> The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. > The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.
@ -18,7 +18,7 @@ $trainDataset = new MnistDataset('train-images-idx3-ubyte', 'train-labels-idx1-u
### Samples and labels ### Samples and labels
To get samples or labels you can use getters: To get samples or labels, you can use getters:
``` ```
$dataset->getSamples(); $dataset->getSamples();

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@ -2,7 +2,7 @@
Helper class that loads data from SVM-Light format file. It extends the `ArrayDataset`. Helper class that loads data from SVM-Light format file. It extends the `ArrayDataset`.
### Constructors Parameters ### Constructor Parameters
* $filepath - (string) path to the file * $filepath - (string) path to the file

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@ -19,7 +19,7 @@ $transformer = new TfIdfTransformer($samples);
### Transformation ### Transformation
To transform a collection of text samples use `transform` method. Example: To transform a collection of text samples, use the `transform` method. Example:
``` ```
use Phpml\FeatureExtraction\TfIdfTransformer; use Phpml\FeatureExtraction\TfIdfTransformer;

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@ -16,7 +16,7 @@ $vectorizer = new TokenCountVectorizer(new WhitespaceTokenizer());
### Transformation ### Transformation
To transform a collection of text samples use `transform` method. Example: To transform a collection of text samples, use the `transform` method. Example:
``` ```
$samples = [ $samples = [
@ -42,7 +42,7 @@ $vectorizer->transform($samples);
### Vocabulary ### Vocabulary
You can extract vocabulary using `getVocabulary()` method. Example: You can extract vocabulary using the `getVocabulary()` method. Example:
``` ```
$vectorizer->getVocabulary(); $vectorizer->getVocabulary();

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@ -5,7 +5,7 @@
## Constructor Parameters ## Constructor Parameters
* $k (int) - number of top features to select, rest will be removed (default: 10) * $k (int) - number of top features to select, rest will be removed (default: 10)
* $scoringFunction (ScoringFunction) - function that take samples and targets and return array with scores (default: ANOVAFValue) * $scoringFunction (ScoringFunction) - function that takes samples and targets and returns an array with scores (default: ANOVAFValue)
```php ```php
use Phpml\FeatureSelection\SelectKBest; use Phpml\FeatureSelection\SelectKBest;
@ -33,7 +33,7 @@ $samples[0] = [1.4, 0.2];
## Scores ## Scores
You can get a array with the calculated score for each feature. You can get an array with the calculated score for each feature.
A higher value means that a given feature is better suited for learning. A higher value means that a given feature is better suited for learning.
Of course, the rating depends on the scoring function used. Of course, the rating depends on the scoring function used.

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@ -1,10 +1,10 @@
# Accuracy # Accuracy
Class for calculate classifier accuracy. Class for calculating classifier accuracy.
### Score ### Score
To calculate classifier accuracy score use `score` static method. Parameters: To calculate classifier accuracy score, use the `score` static method. Parameters:
* $actualLabels - (array) true sample labels * $actualLabels - (array) true sample labels
* $predictedLabels - (array) predicted labels (e.x. from test group) * $predictedLabels - (array) predicted labels (e.x. from test group)

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@ -1,6 +1,6 @@
# Classification Report # Classification Report
Class for calculate main classifier metrics: precision, recall, F1 score and support. Class for calculating main classifier metrics: precision, recall, F1 score and support.
### Report ### Report

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@ -1,6 +1,6 @@
# Confusion Matrix # Confusion Matrix
Class for compute confusion matrix to evaluate the accuracy of a classification. Class for computing confusion matrix to evaluate the accuracy of a classification.
### Example (all targets) ### Example (all targets)

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@ -39,8 +39,7 @@ $mlp = new MLPClassifier(4, [$layer1, $layer2], ['a', 'b', 'c']);
## Train ## Train
To train a MLP simply provide train samples and labels (as array). Example: To train a MLP, simply provide train samples and labels (as array). Example:
``` ```
$mlp->train( $mlp->train(
@ -71,7 +70,7 @@ $mlp->setLearningRate(0.1);
## Predict ## Predict
To predict sample label use predict method. You can provide one sample or array of samples: To predict sample label use the `predict` method. You can provide one sample or array of samples:
``` ```
$mlp->predict([[1, 1, 1, 1], [0, 0, 0, 0]]); $mlp->predict([[1, 1, 1, 1], [0, 0, 0, 0]]);

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@ -49,7 +49,7 @@ $data = [
``` ```
You can also use `$samples` constructer parameter instead of `fit` method: You can also use the `$samples` constructor parameter instead of the `fit` method:
``` ```
use Phpml\Preprocessing\Imputer; use Phpml\Preprocessing\Imputer;

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@ -1,10 +1,10 @@
# LeastSquares Linear Regression # LeastSquares Linear Regression
Linear model that use least squares method to approximate solution. Linear model that uses least squares method to approximate solution.
### Train ### Train
To train a model simply provide train samples and targets values (as `array`). Example: To train a model, simply provide train samples and targets values (as `array`). Example:
``` ```
$samples = [[60], [61], [62], [63], [65]]; $samples = [[60], [61], [62], [63], [65]];
@ -18,7 +18,7 @@ You can train the model using multiple data sets, predictions will be based on a
### Predict ### Predict
To predict sample target value use `predict` method with sample to check (as `array`). Example: To predict sample target value, use the `predict` method with sample to check (as `array`). Example:
``` ```
$regression->predict([64]); $regression->predict([64]);
@ -28,7 +28,7 @@ $regression->predict([64]);
### Multiple Linear Regression ### Multiple Linear Regression
The term multiple attached to linear regression means that there are two or more sample parameters used to predict target. The term multiple attached to linear regression means that there are two or more sample parameters used to predict target.
For example you can use: mileage and production year to predict price of a car. For example you can use: mileage and production year to predict the price of a car.
``` ```
$samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]]; $samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
@ -42,7 +42,7 @@ $regression->predict([60000, 1996])
### Intercept and Coefficients ### Intercept and Coefficients
After you train your model you can get the intercept and coefficients array. After you train your model, you can get the intercept and coefficients array.
``` ```
$regression->getIntercept(); $regression->getIntercept();

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@ -21,7 +21,7 @@ $regression = new SVR(Kernel::LINEAR, $degree = 3, $epsilon=10.0);
### Train ### Train
To train a model simply provide train samples and targets values (as `array`). Example: To train a model, simply provide train samples and targets values (as `array`). Example:
``` ```
use Phpml\Regression\SVR; use Phpml\Regression\SVR;
@ -38,7 +38,7 @@ You can train the model using multiple data sets, predictions will be based on a
### Predict ### Predict
To predict sample target value use `predict` method. You can provide one sample or array of samples: To predict sample target value, use the `predict` method. You can provide one sample or array of samples:
``` ```
$regression->predict([64]) $regression->predict([64])

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@ -6,12 +6,11 @@ In machine learning, it is common to run a sequence of algorithms to process and
* Convert each documents words into a numerical feature vector ([Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)). * Convert each documents words into a numerical feature vector ([Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)).
* Learn a prediction model using the feature vectors and labels. * Learn a prediction model using the feature vectors and labels.
PHP-ML represents such a workflow as a Pipeline, which consists sequence of transformers and a estimator. PHP-ML represents such a workflow as a Pipeline, which consists of a sequence of transformers and an estimator.
### Constructor Parameters ### Constructor Parameters
* $transformers (array|Transformer[]) - sequence of objects that implements Transformer interface * $transformers (array|Transformer[]) - sequence of objects that implements the Transformer interface
* $estimator (Estimator) - estimator that can train and predict * $estimator (Estimator) - estimator that can train and predict
``` ```
@ -29,7 +28,8 @@ $pipeline = new Pipeline($transformers, $estimator);
### Example ### Example
First our pipeline replace missing value, then normalize samples and finally train SVC estimator. Thus prepared pipeline repeats each transformation step for predicted sample. First, our pipeline replaces the missing value, then normalizes samples and finally trains the SVC estimator.
Thus prepared pipeline repeats each transformation step for predicted sample.
``` ```
use Phpml\Classification\SVC; use Phpml\Classification\SVC;

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@ -4,7 +4,7 @@ Selected algorithms require the use of a function for calculating the distance.
### Euclidean ### Euclidean
Class for calculation Euclidean distance. Class for calculating Euclidean distance.
![euclidean](https://upload.wikimedia.org/math/8/4/9/849f040fd10bb86f7c85eb0bbe3566a4.png "Euclidean Distance") ![euclidean](https://upload.wikimedia.org/math/8/4/9/849f040fd10bb86f7c85eb0bbe3566a4.png "Euclidean Distance")
@ -21,7 +21,7 @@ $euclidean->distance($a, $b);
### Manhattan ### Manhattan
Class for calculation Manhattan distance. Class for calculating Manhattan distance.
![manhattan](https://upload.wikimedia.org/math/4/c/5/4c568bd1d76a6b15e19cb2ac3ad75350.png "Manhattan Distance") ![manhattan](https://upload.wikimedia.org/math/4/c/5/4c568bd1d76a6b15e19cb2ac3ad75350.png "Manhattan Distance")
@ -38,7 +38,7 @@ $manhattan->distance($a, $b);
### Chebyshev ### Chebyshev
Class for calculation Chebyshev distance. Class for calculating Chebyshev distance.
![chebyshev](https://upload.wikimedia.org/math/7/1/2/71200f7dbb43b3bcfbcbdb9e02ab0a0c.png "Chebyshev Distance") ![chebyshev](https://upload.wikimedia.org/math/7/1/2/71200f7dbb43b3bcfbcbdb9e02ab0a0c.png "Chebyshev Distance")
@ -55,7 +55,7 @@ $chebyshev->distance($a, $b);
### Minkowski ### Minkowski
Class for calculation Minkowski distance. Class for calculating Minkowski distance.
![minkowski](https://upload.wikimedia.org/math/a/a/0/aa0c62083c12390cb15ac3217de88e66.png "Minkowski Distance") ![minkowski](https://upload.wikimedia.org/math/a/a/0/aa0c62083c12390cb15ac3217de88e66.png "Minkowski Distance")
@ -83,7 +83,7 @@ $minkowski->distance($a, $b);
### Custom distance ### Custom distance
To apply your own function of distance use `Distance` interface. Example To apply your own function of distance use the `Distance` interface. Example:
``` ```
class CustomDistance implements Distance class CustomDistance implements Distance