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restructured and addition of ROCKET and MINIROCKET
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‎docs/index.rst

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source/NSW_Demo/NSWClassifier_Demo.rst
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source/kNNClassifier_Demo/kNNClassifier_Demo.rst
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source/MACFAC_Demo/MACFAC_Demo.rst
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source/MINIROCKET_Demo/MINIROCKET_Demo.rst
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Models

‎docs/source/AnnoyClassifier_Demo/AnnoyClassifier_Demo.rst

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.. code:: ipython3
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from mlots import AnnoyClassifier
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from mlots.models import AnnoyClassifier
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from sklearn.model_selection import GridSearchCV
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from scipy.io import arff
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import matplotlib.pyplot as plt

‎docs/source/MACFAC_Demo/MACFAC_Demo.rst

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.. code:: ipython3
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from mlots import kNNClassifier
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from mlots.models import kNNClassifier
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from sklearn.model_selection import GridSearchCV
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from scipy.io import arff
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import matplotlib.pyplot as plt

‎docs/source/MINIROCKET_Demo/MINIROCKET_Demo.rst

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MINIROCKET
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==========
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Dempster et al. https://arxiv.org/abs/2012.08791
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Importing Packages
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------------------
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.. code:: ipython3
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from mlots.models import RidgeClassifierCV
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from mlots.transformation import MINIROCKET
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from scipy.io import arff
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import numpy as np
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import warnings
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warnings.filterwarnings("ignore")
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from sklearn.metrics import accuracy_score
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Loading Data
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------------
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| Here we are loading the ``SmoothSubspace`` dataset.
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| The datasets are in two ``.arff`` files with pre-defined train and
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test splits.
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| The following code reads the two files stores the ``X`` (time-series
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data) and ``y`` (labels), into their specific train and test sets.
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\**\*
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.. code:: ipython3
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name = "SmoothSubspace"
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dataset = arff.loadarff(f'input/{name}/{name}_TRAIN.arff'.format(name=name))[0]
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X_train = np.array(dataset.tolist(), dtype=np.float32)
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y_train = X_train[: , -1]
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X_train = X_train[:, :-1]
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dataset = arff.loadarff(f'input/{name}/{name}_TEST.arff'.format(name=name))[0]
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X_test = np.array(dataset.tolist(), dtype=np.float32)
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y_test = X_test[: , -1]
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X_test = X_test[:, :-1]
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#Converting target from bytes to integer
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y_train = [int.from_bytes(el, "little") for el in y_train]
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y_test = [int.from_bytes(el, "little") for el in y_test]
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X_train.shape, X_test.shape
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.. parsed-literal::
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((150, 15), (150, 15))
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===== =========== =========
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Set Sample size TS length
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===== =========== =========
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Train 150 15
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Test 150 15
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===== =========== =========
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Transforming Data using ``MINIROCKET``
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--------------------------------------
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.. code:: ipython3
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print("Shape of X_train and X_test before transformation: ",X_train.shape,", ",X_test.shape)
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.. parsed-literal::
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Shape of X_train and X_test before transformation: (150, 15) , (150, 15)
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.. code:: ipython3
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minirocket = MINIROCKET()
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minirocket.fit(X_train)
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X_train = minirocket.transform(X_train)
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X_test = minirocket.transform(X_test)
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.. code:: ipython3
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print("Shape of X_train and X_test before transformation: ",X_train.shape,", ",X_test.shape)
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.. parsed-literal::
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Shape of X_train and X_test before transformation: (150, 9996) , (150, 9996)
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Classification
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--------------
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| We can employ ``RidgeClassifierCV`` as our linear model for the
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classification task.
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| \**\*
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.. code:: ipython3
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model = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True)
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model = model.fit(X_train, y_train)
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.. code:: ipython3
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acc = model.score(X_test, y_test)
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print(f"Model accuracy: {acc:.2f}%")
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.. parsed-literal::
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Model accuracy: 0.95%
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‎docs/source/NSW_Demo/NSWClassifier_Demo.rst

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.. code:: ipython3
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from mlots import NSWClassifier
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from mlots.models import NSWClassifier
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from sklearn.model_selection import GridSearchCV
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from scipy.io import arff
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import matplotlib.pyplot as plt

‎docs/source/kNNClassifier_Demo/kNNClassifier_Demo.rst

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.. code:: ipython3
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from mlots import kNNClassifier
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from mlots.models import kNNClassifier
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from sklearn.model_selection import GridSearchCV
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from scipy.io import arff
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import matplotlib.pyplot as plt

‎docs/source/mlots.models.rst

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mlots.models package
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====================
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mlots.models.annoy module
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-------------------------
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Module contents
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---------------
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.. automodule:: mlots.models.annoy
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.. automodule:: mlots.models
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:members:
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:undoc-members:
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:show-inheritance:
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mlots.models.hnsw module
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------------------------
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.. automodule:: mlots.models.hnsw
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:members:
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:undoc-members:
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:show-inheritance:
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mlots.models.knn module
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-----------------------
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.. automodule:: mlots.models.knn
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:members:
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:undoc-members:
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:show-inheritance:
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mlots.models.nsw module
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-----------------------
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.. automodule:: mlots.models.nsw
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:members:
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:undoc-members:
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:show-inheritance:
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‎docs/source/mlots.rst

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mlots.models
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mlots.transformation
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Submodules
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----------
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mlots.utilities module
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----------------------
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.. automodule:: mlots.utilities
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: mlots
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/mlots.transformation.rst

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mlots.transformation package
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============================
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mlots.transformation.MINIROCKET module
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--------------------------------------
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Module contents
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---------------
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.. automodule:: mlots.transformation.MINIROCKET
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:members:
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:undoc-members:
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:show-inheritance:
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mlots.transformation.ROCKET module
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----------------------------------
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.. automodule:: mlots.transformation.ROCKET
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.. automodule:: mlots.transformation
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:members:
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:undoc-members:
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:show-inheritance:

‎mlots/__init__.py

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from mlots.models.nsw import NSWClassifier
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from mlots.models.annoy import AnnoyClassifier
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from mlots.models.hnsw import HNSWClassifier
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from mlots.models.knn import kNNClassifier
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from mlots.models.knn import kNNClassifier_CustomDist
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from mlots.transformation.rocket import ROCKET
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from mlots.transformation.minirocket import MINIROCKET
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV
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from mlots.utilities import from_pandas_dataframe
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__all__ = [
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"AnnoyClassifier", "HNSWClassifier", "kNNClassifier",
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"kNNClassifier_CustomDist", "NSWClassifier", "RidgeClassifier", "RidgeClassifierCV",
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"ROCKET", "MINIROCKET", "from_pandas_dataframe"]
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from .utilities import from_pandas_dataframe

‎mlots/models/__init__.py

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from .annoy import AnnoyClassifier
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from .hnsw import HNSWClassifier
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from .knn import kNNClassifier
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from .knn import kNNClassifier_CustomDist
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from .nsw import NSWClassifier
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from mlots.models._annoy import AnnoyClassifier
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from mlots.models._hnsw import HNSWClassifier
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from mlots.models._knn import kNNClassifier, kNNClassifier_CustomDist
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from mlots.models._nsw import NSWClassifier
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV
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__all__ = [

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‎mlots/models/hnsw.py renamed to ‎mlots/models/_hnsw.py

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‎mlots/models/knn.py renamed to ‎mlots/models/_knn.py

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‎mlots/models/nsw.py renamed to ‎mlots/models/_nsw.py

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‎mlots/setup.py

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import os
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def configuration(parent_package='', top_path=None):
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from numpy.distutils.misc_util import Configuration
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libraries = []
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if os.name == 'posix':
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libraries.append('m')
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config = Configuration('mlots', parent_package, top_path)
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config.add_subpackage('models')
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config.add_subpackage('transformation')
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return config
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if __name__ == '__main__':
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from numpy.distutils.core import setup
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setup(**configuration(top_path='').todict())

‎mlots/transformation/__init__.py

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from .minirocket import MINIROCKET
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from .rocket import ROCKET
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from ._minirocket import MINIROCKET
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from ._rocket import ROCKET
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__all__ = ["MINIROCKET", "ROCKET"]

‎mlots/transformation/minirocket.py renamed to ‎mlots/transformation/_minirocket.py

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‎notebooks/AnnoyClassifier_Demo.ipynb

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},
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"outputs": [],
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"source": [
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"from mlots import AnnoyClassifier\n",
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"from mlots.models import AnnoyClassifier\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from scipy.io import arff\n",
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"import matplotlib.pyplot as plt\n",

‎notebooks/MACFAC_Demo.ipynb

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},
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"outputs": [],
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"source": [
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"from mlots import kNNClassifier\n",
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"from mlots.models import kNNClassifier\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from scipy.io import arff\n",
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"import matplotlib.pyplot as plt\n",

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