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Fixed docstring warnings - could still be mistakes

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James McMurray 2013-09-20 13:38:20 +01:00
parent c36a6b341c
commit be3880c0bd
20 changed files with 261 additions and 144 deletions
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31
GPy/examples/classification.py
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@ -11,13 +11,15 @@ import GPy
default_seed = 10000
def crescent_data(seed=default_seed, kernel=None): # FIXME
"""Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
"""
Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
:param seed : seed value for data generation.
:param seed: seed value for data generation.
:type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:param inducing: number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int
"""
data = GPy.util.datasets.crescent_data(seed=seed)
@ -35,6 +37,7 @@ def crescent_data(seed=default_seed, kernel=None): # FIXME
def oil(num_inducing=50, max_iters=100, kernel=None):
"""
Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
"""
data = GPy.util.datasets.oil()
X = data['X']
@ -64,8 +67,10 @@ def oil(num_inducing=50, max_iters=100, kernel=None):
def toy_linear_1d_classification(seed=default_seed):
"""
Simple 1D classification example
:param seed : seed value for data generation (default is 4).
:param seed: seed value for data generation (default is 4).
:type seed: int
"""
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
@ -92,8 +97,10 @@ def toy_linear_1d_classification(seed=default_seed):
def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
"""
Sparse 1D classification example
:param seed : seed value for data generation (default is 4).
:param seed: seed value for data generation (default is 4).
:type seed: int
"""
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
@ -123,10 +130,11 @@ def sparse_crescent_data(num_inducing=10, seed=default_seed, kernel=None):
Run a Gaussian process classification with DTC approxiamtion on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
:param seed : seed value for data generation.
:param seed: seed value for data generation.
:type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:param inducing: number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int
"""
data = GPy.util.datasets.crescent_data(seed=seed)
@ -147,10 +155,11 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
Run a Gaussian process classification with FITC approximation on the crescent data. The demonstration uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
:param seed : seed value for data generation.
:param seed: seed value for data generation.
:type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:param inducing: number of inducing variables (only used for 'FITC' or 'DTC').
:type num_inducing: int
"""
data = GPy.util.datasets.crescent_data(seed=seed)
@ -171,8 +180,10 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
def toy_heaviside(seed=default_seed):
"""
Simple 1D classification example using a heavy side gp transformation
:param seed : seed value for data generation (default is 4).
:param seed: seed value for data generation (default is 4).
:type seed: int
"""
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)