Adaline
Adaline(eta=0.01, epochs=50, minibatches=None, random_seed=None, print_progress=0)
ADAptive LInear NEuron classifier.
Note that this implementation of Adaline expects binary class labels in {0, 1}.
Parameters
-
eta
: float (default: 0.01)solver rate (between 0.0 and 1.0)
-
epochs
: int (default: 50)Passes over the training dataset. Prior to each epoch, the dataset is shuffled if
minibatches > 1
to prevent cycles in stochastic gradient descent. -
minibatches
: int (default: None)The number of minibatches for gradient-based optimization. If None: Normal Equations (closed-form solution) If 1: Gradient Descent learning If len(y): Stochastic Gradient Descent (SGD) online learning If 1 < minibatches < len(y): SGD Minibatch learning
-
random_seed
: int (default: None)Set random state for shuffling and initializing the weights.
-
print_progress
: int (default: 0)Prints progress in fitting to stderr if not solver='normal equation' 0: No output 1: Epochs elapsed and cost 2: 1 plus time elapsed 3: 2 plus estimated time until completion
Attributes
-
w_
: 2d-array, shape={n_features, 1}Model weights after fitting.
-
b_
: 1d-array, shape={1,}Bias unit after fitting.
-
cost_
: listSum of squared errors after each epoch.
Examples
For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/classifier/Adaline/
Methods
fit(X, y, init_params=True)
Learn model from training data.
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y
: array-like, shape = [n_samples]Target values.
-
init_params
: bool (default: True)Re-initializes model parameters prior to fitting. Set False to continue training with weights from a previous model fitting.
Returns
self
: object
get_params(deep=True)
Get parameters for this estimator.
Parameters
-
deep
: boolean, optionalIf True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
-
params
: mapping of string to anyParameter names mapped to their values.'
adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
Author: Gael Varoquaux gael.varoquaux@normalesup.org
License: BSD 3 clause
predict(X)
Predict targets from X.
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
Returns
-
target_values
: array-like, shape = [n_samples]Predicted target values.
score(X, y)
Compute the prediction accuracy
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y
: array-like, shape = [n_samples]Target values (true class labels).
Returns
-
acc
: floatThe prediction accuracy as a float between 0.0 and 1.0 (perfect score).
set_params(params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it's possible to update each
component of a nested object.
Returns
self
adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py