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A `Node` is the basic building block of an MDP application. It represents a data processing element, like for example a learning algorithm, a data filter, or a visualization step. Each node can have one or more training phases, during which the internal structures are learned from training data (e.g. the weights of a neural network are adapted or the covariance matrix is estimated) and an execution phase, where new data can be processed forwards (by processing the data through the node) or backwards (by applying the inverse of the transformation computed by the node if defined). Nodes have been designed to be applied to arbitrarily long sets of data: if the underlying algorithms supports it, the internal structures can be updated incrementally by sending multiple batches of data (this is equivalent to online learning if the chunks consists of single observations, or to batch learning if the whole data is sent in a single chunk). It is thus possible to perform computations on amounts of data that would not fit into memory or to generate data on-the-fly. A `Node` also defines some utility methods, like for example `copy` and `save`, that return an exact copy of a node and save it in a file, respectively. Additional methods may be present, depending on the algorithm. `Node` subclasses should take care of overwriting (if necessary) the functions `is_trainable`, `_train`, `_stop_training`, `_execute`, `is_invertible`, `_inverse`, `_get_train_seq`, and `_get_supported_dtypes`. If you need to overwrite the getters and setters of the node's properties refer to the docstring of `get_input_dim`/`set_input_dim`, `get_output_dim`/`set_output_dim`, and `get_dtype`/`set_dtype`.
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Inherited from Inherited from |
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_train_seq List of tuples:: |
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dtype dtype |
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input_dim Input dimensions |
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output_dim Output dimensions |
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supported_dtypes Supported dtypes |
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Inherited from |
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Calling an instance of `Node` is equivalent to calling its `execute` method. |
If the input dimension and the output dimension are unspecified, they will be set when the `train` or `execute` method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of `train` or `execute`. Every subclass must take care of up- or down-casting the internal structures to match this argument (use `_refcast` private method when possible).
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repr(x)
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str(x)
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Return the list of dtypes supported by this node. The types can be specified in any format allowed by :numpy:`dtype`. |
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This method contains all pre-execution checks. It can be used when a subclass defines multiple execution methods. |
This method contains all pre-inversion checks. It can be used when a subclass defines multiple inversion methods. |
Helper function to cast arrays to the internal dtype. |
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Return a deep copy of the node. :param protocol: the pickle protocol (deprecated). |
Process the data contained in `x`. If the object is still in the training phase, the function `stop_training` will be called. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_execute` to implement their execution phase. The docstring of the `_execute` method overwrites this docstring. |
Return the index of the current training phase. The training phases are defined in the list `self._train_seq`. |
Return dtype. |
Return input dimensions. |
Return output dimensions. |
Return the number of training phases still to accomplish. If the node is not trainable then return 0. |
Return dtypes supported by the node as a list of :numpy:`dtype` objects. Note that subclasses should overwrite `self._get_supported_dtypes` when needed. |
Return True if the node has multiple training phases. |
Invert `y`. If the node is invertible, compute the input ``x`` such that ``y = execute(x)``. By default, subclasses should overwrite `_inverse` to implement their `inverse` function. The docstring of the `inverse` method overwrites this docstring. |
Return True if the node can be inverted, False otherwise. |
Return True if the node can be trained, False otherwise. |
Return True if the node is in the training phase, False otherwise. |
Save a pickled serialization of the node to `filename`. If `filename` is None, return a string. Note: the pickled `Node` is not guaranteed to be forwards or backwards compatible. |
Set internal structures' dtype. Perform sanity checks and then calls ``self._set_dtype(n)``, which is responsible for setting the internal attribute ``self._dtype``. Note that subclasses should overwrite `self._set_dtype` when needed. |
Set input dimensions. Perform sanity checks and then calls ``self._set_input_dim(n)``, which is responsible for setting the internal attribute ``self._input_dim``. Note that subclasses should overwrite `self._set_input_dim` when needed. |
Set output dimensions. Perform sanity checks and then calls ``self._set_output_dim(n)``, which is responsible for setting the internal attribute ``self._output_dim``. Note that subclasses should overwrite `self._set_output_dim` when needed. |
Stop the training phase. By default, subclasses should overwrite `_stop_training` to implement this functionality. The docstring of the `_stop_training` method overwrites this docstring. |
Update the internal structures according to the input data `x`. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_train` to implement their training phase. The docstring of the `_train` method overwrites this docstring. Note: a subclass supporting multiple training phases should implement the *same* signature for all the training phases and document the meaning of the arguments in the `_train` method doc-string. Having consistent signatures is a requirement to use the node in a flow. |
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_train_seqList of tuples:: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ...] By default:: _train_seq = [(self._train, self._stop_training)]
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dtypedtype |
input_dimInput dimensions
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output_dimOutput dimensions
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supported_dtypesSupported dtypes
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