Todo
The parameter(s) ‘A, c’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.initialize_dual_lambda_mu, line 4.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.is_feasible, line 4.)
Todo
This documentation seems wrong since lambda is not in the list of parameters.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.is_in_v, line 4.)
Todo
The parameter(s) ‘mu, theta, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.is_in_v, line 7.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.is_in_v_s, line 4.)
Todo
The parameter(s) ‘A, b, c, x0’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.solve, line 3.)
Todo
The return value(s) ‘x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.solve, line 5.)
Todo
Document parameter labmda
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.solve, line 11.)
Todo
Document parameter mu
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPoint.solve, line 15.)
Todo
The parameter(s) ‘A, c’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.initialize_dual_lambda_mu, line 4.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.is_feasible, line 4.)
Todo
This documentation looks wrong since lambda is not part of the parameters
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.is_in_v, line 4.)
Todo
The parameter(s) ‘gamma, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.is_in_v, line 7.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.is_in_v_s, line 4.)
Todo
The parameter(s) ‘A, b, c, x0’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.solve, line 3.)
Todo
The return value(s) ‘x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.solve, line 5.)
Todo
Document parameter labmda
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.solve, line 11.)
Todo
Document parameter mu
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointLongstep.solve, line 15.)
Todo
The parameter(s) ‘A, c’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.initialize_dual_lambda_mu, line 4.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.is_feasible, line 4.)
Todo
This documentation seems wrong since lambda is not in the list of parameters.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.is_in_v, line 4.)
Todo
The parameter(s) ‘mu, theta, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.is_in_v, line 7.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.is_in_v_s, line 4.)
Todo
The parameter(s) ‘A, b, c, x0’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.solve, line 3.)
Todo
The return value(s) ‘x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.solve, line 5.)
Todo
Document parameter labmda
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.solve, line 11.)
Todo
Document parameter mu
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointPredictorCorrector.solve, line 15.)
Todo
The parameter(s) ‘A, c’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.initialize_dual_lambda_mu, line 4.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.is_feasible, line 4.)
Todo
This documentation seems wrong since lambda is not in the list of parameters.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.is_in_v, line 4.)
Todo
The parameter(s) ‘mu, theta, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.is_in_v, line 7.)
Todo
The parameter(s) ‘A, b, c, lambda, mu, x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.is_in_v_s, line 4.)
Todo
The parameter(s) ‘A, b, c, x0’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.solve, line 3.)
Todo
The return value(s) ‘x’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.solve, line 5.)
Todo
Document parameter labmda
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.solve, line 11.)
Todo
Document parameter mu
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.LPInteriorPointShortstep.solve, line 15.)
Todo
Explain, what width means in this case
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.pavxWidth, line 25.)
Todo
Explain, what width means in this case
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.pavxWidthHeight, line 26.)
Todo
Explain, what height means in this case
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/0/bob.math/bob/math/__init__.py:docstring of bob.math.pavxWidthHeight, line 32.)
Todo
Explain DCTFeatures constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.DCTFeatures, line 14.)
Todo
The parameter(s) ‘levels, max_level, min_level, quantization_table’ are used, but not documented.
Parameters:
dtype : numpy.dtype
[default: numpy.uint8] The data-type for the GLCM class
glcm : bob.ip.base.GLCM
The GLCM object to use for copy-construction
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.GLCM, line 18.)
Todo
Explain GaussianScaleSpace constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.GaussianScaleSpace, line 13.)
Todo
UPDATE as this is not true
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.LBPTop, line 7.)
Todo
Add documentation for MultiscaleRetinex
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.MultiscaleRetinex, line 13.)
Todo
Check if this documentation is correct (seems to be copied from bob.ip.base.SelfQuotientImage
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.MultiscaleRetinex.process, line 4.)
Todo
Explain SIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.SIFT, line 13.)
Todo
explain SelfQuotientImage constructor
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.SelfQuotientImage, line 15.)
Todo
Explain TanTriggs constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.TanTriggs, line 13.)
Todo
Explain VLDSIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLDSIFT, line 12.)
Todo
Describe the output of the VLDSIFT.extract() method in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLDSIFT.extract, line 8.)
Todo
Explain VLSIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLSIFT, line 12.)
Todo
explain WeightedGaussian constructor
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.WeightedGaussian, line 13.)
Todo
The parameter(s) ‘img, mask’ are used, but not documented.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.extrapolate_mask, line 19.)
Todo
Explain gamma correction in more detail
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.gamma_correction, line 3.)
Todo
Adapt http://pypi.python.org/pypi/xbob.example.faceverify so that it actually uses the bob.ip.gabor package.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.ip.gabor/doc/guide.rst, line 187.)
Todo
Support for weight cost in multi-class classification?
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.learn.libsvm/doc/c_cpp_api.rst, line 445.)
Todo
Describe the C++ API of this package.
(The original entry is located in /home/travis/build/bioidiap/bob.python/layers/1/bob.learn.mlp/doc/c_cpp_api.rst, line 245.)
The Iris flower data set or Fisher’s Iris data set is a multivariate data set introduced by Sir Ronald Aylmer Fisher (1936) as an example of discriminant analysis. The dataset consists of 50 samples from three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample, they are the length and the width of sepal and petal, in centimeters.
As this data set is quite small and used for testing purpose, it is directly integrated into Bob, which provides both ways to access the data, as well as the data itself (feature vectors of length four for various samples of the three species).
A description of the feature vector can be obtained using the attribute bob.db.iris.names.
>>> descriptor_labels = bob.db.iris.names
>>> descriptor_labels
['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']
The data (feature vectors) can be retrieved using the bob.db.iris.data() function. This returns a 3-key dictionary, with 3 numpy.ndarray as values, one for each of the three species of Iris flowers.
>>> data = bob.db.iris.data()
>>> type(data['setosa'])
<... 'numpy.ndarray'>
>>> data['setosa'].shape
(50, 4)
>>> list(data.keys())
[...]
Each numpy.ndarray consists of 50 feature vectors of length four.
The database also contains statistics about the feature vectors, which can be obtained using the bob.db.iris.stats dictionary. A description of these statistics is provided by bob.db.iris.stat_names.
The Iris flower data set or Fisher’s Iris data set is a multivariate data set introduced by Sir Ronald Aylmer Fisher (1936) as an example of discriminant analysis. It is sometimes called Anderson’s Iris data set because Edgar Anderson collected the data to quantify the geographic variation of Iris flowers in the Gaspe Peninsula.
For more information: http://en.wikipedia.org/wiki/Iris_flower_data_set
References:
1. Fisher,R.A. “The use of multiple measurements in taxonomic problems”, Annual Eugenics, 7, Part II, 179-188 (1936); also in “Contributions to Mathematical Statistics” (John Wiley, NY, 1950).
2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
3. Dasarathy, B.V. (1980) “Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71.
4. Gates, G.W. (1972) “The Reduced Nearest Neighbor Rule”. IEEE Transactions on Information Theory, May 1972, 431-433.
Names of the features for each entry in the dataset.
These are basic statistics for each of the features in the whole dataset.
These are the statistics available in each column of the stats variable.
Loads from (text) file and returns Fisher’s Iris Dataset.
This set is small and simple enough to require an SQL backend. We keep the single file it has in text and load it on-the-fly every time this method is called.
We return a dictionary containing the 3 classes of Iris plants catalogued in this dataset. Each dictionary entry contains an 2D numpy.ndarray of 64-bit floats and 50 entries. Each entry is an Array with 4 features as described by “names”.