This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| *.pyc |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| *.npy | |
| *.pkl |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import inspect | |
| from pickle import loads, dumps | |
| from IPython.utils import pickleutil | |
| def class_dumps(cls): | |
| canned_dict = pickleutil.canDict( | |
| dict((k, v) for k, v in cls.__dict__.items() | |
| if k not in ('__weakref__', '__dict__'))) | |
| parents = tuple(cls.mro()) |
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| .classpath | |
| .project | |
| .settings/ | |
| target/ |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| *.pyc | |
| *.npy | |
| *.pkl | |
| *.mmap |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #!/usr/bin/env python | |
| import os | |
| import sys | |
| import logging | |
| from azure import WindowsAzureConflictError | |
| from azure import WindowsAzureError | |
| from azure.servicemanagement import ServiceManagementService | |
| from azure.servicemanagement import OSVirtualHardDisk |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ====================================================================== | |
| FAIL: Doctest: sklearn.ensemble.partial_dependence.partial_dependence | |
| ---------------------------------------------------------------------- | |
| Traceback (most recent call last): | |
| File "/usr/lib/python3.3/doctest.py", line 2154, in runTest | |
| raise self.failureException(self.format_failure(new.getvalue())) | |
| AssertionError: Failed doctest test for sklearn.ensemble.partial_dependence.partial_dependence | |
| File "/home/vagrant/venvs/venv3/lib/python3.3/site-packages/sklearn/ensemble/partial_dependence.py", line 76, in partial_dependence | |
| ---------------------------------------------------------------------- |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| x |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import os | |
| import numpy as np | |
| import bloscpack as bp | |
| from sklearn.datasets import fetch_mldata | |
| mnist = fetch_mldata('MNIST original') | |
| data = mnist.data.astype(np.float32) / 255. | |
| print("dtype: {}".format(data.dtype)) | |
| print("shape: {}".format(data.shape)) |