Multivariate Pattern Analysis in Python

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# mvpa2.testing.clfs.kNN

class `mvpa2.testing.clfs.``kNN`(k=2, dfx=<function squared_euclidean_distance at 0xdfcca04>, voting='weighted', **kwargs)

  k-Nearest-Neighbour classifier.

  This is a simple classifier that bases its decision on the distances between the training dataset samples and the test sample(s). Distances are computed using a customizable distance function. A certain number (`k`)of nearest neighbors is selected based on the smallest distances and the labels of this neighboring samples are fed into a voting function to determine the labels of the test sample.

  Training a kNN classifier is extremely quick, as no actual training is performed as the training dataset is simply stored in the classifier. All computations are done during classifier prediction.

  Notes

  If enabled, kNN stores the votes per class in the ‘values’ state after calling predict().

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `distances`: Distances computed for each sample
  - `estimates+`: Internal classifier estimates the most recent predictions are based on
  - `predicting_time+`: Time (in seconds) which took classifier to predict
  - `predictions+`: Most recent set of predictions
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `trained_dataset`: The dataset it has been trained on
  - `trained_nsamples+`: Number of samples it has been trained on
  - `trained_targets+`: Set of unique targets it has been trained on
  - `training_stats`: Confusion matrix of learning performance
  - `training_time+`: Time (in seconds) it took to train the learner

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(**kwargs) | Factory method to return an appropriate sensitivity analyzer for the respective classifier.
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() | Providing summary over the classifier
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Parameters:

  k : unsigned integer

  > Number of nearest neighbours to be used for voting.

  dfx : functor

  > Function to compute the distances between training and test samples. Default: squared euclidean distance

  voting : str

  > Voting method used to derive predictions from the nearest neighbors. Possible values are ‘majority’ (simple majority of classes determines vote) and ‘weighted’ (votes are weighted according to the relative frequencies of each class in the training data).

  enable_ca : None or list of str

  > Names of the conditional attributes which should be enabled in addition to the default ones

  disable_ca : None or list of str

  > Names of the conditional attributes which should be disabled

  auto_train : bool

  > Flag whether the learner will automatically train itself on the input dataset when called untrained.

  force_train : bool

  > Flag whether the learner will enforce training on the input dataset upon every call.

  space: str, optional :

  > Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

  postproc : Node instance, optional

  > Node to perform post-processing of results. This node is applied in `__call__()` to perform a final processing step on the to be result dataset. If None, nothing is done.

  descr : str

  > Description of the instance

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(**kwargs) | Factory method to return an appropriate sensitivity analyzer for the respective classifier.
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() | Providing summary over the classifier
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  `dfx`

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