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UPDATE: version number, README, CHANGES.txt

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Madison May
2015-07-28 17:11:30 -04:00
parent d00b669a16
commit b51e374efa
6 changed files with 18 additions and 23 deletions
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README.rst
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@@ -91,15 +91,15 @@ Examples
Batch API
---------
Each ``indicoio`` function has a corresponding batch function for
analyzing many examples with a single request. Simply pass in a list of
inputs and receive a list of results in return.
Each ``indicoio`` function can process many examples with a single
request. Simply pass in a list of inputs and receive a list of results
in return.
.. code:: python
>>> from indicoio import batch_sentiment
>>> from indicoio import sentiment
>>> batch_sentiment(['Best day ever', 'Worst day ever'])
>>> sentiment(['Best day ever', 'Worst day ever'])
[0.9899001220871786, 0.005709885173415242]
Calling multiple APIs with a single function
@@ -119,12 +119,12 @@ Accepted image API names: ``fer, facial_features, image_features``
.. code:: python
>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
>>> from indicoio import predict_text, predict_image, predict_text, predict_image
>>> predict_text('Best day ever', apis=["sentiment", "language"])
{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
>>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
>>> predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
{'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
>>> import numpy as np
@@ -134,6 +134,6 @@ Accepted image API names: ``fer, facial_features, image_features``
>>> predict_image(test_face, apis=["fer", "facial_features"])
{'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
>>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
>>> predict_image([test_face, test_face], apis=["fer", "facial_features"])
{'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}