bob.machine.KMeansMachine¶

class bob.machine.KMeansMachine((object)self) → None¶

Bases: bob.machine._machine.MachineDoubleBase

This class implements a k-means classifier. See Section 9.1 of Bishop, “Pattern recognition and machine learning”, 2006

__init__( (object)self, (int)n_means, (int)n_inputs) -> None

__init__( (object)arg1, (KMeansMachine)self) -> None

__init__( (object)self, (HDF5File)config) -> None

__init__((object)self) → None¶

__init__( (object)self, (int)n_means, (int)n_inputs) -> None

__init__( (object)arg1, (KMeansMachine)self) -> None

__init__( (object)self, (HDF5File)config) -> None

Methods

`__init__`((object)self)	__init__( (object)self, (int)n_means, (int)n_inputs) -> None
`forward`((MachineDoubleBase)self, (object)input)	Executes the machine on the given 1D numpy array of float64, and returns the output.
`forward_`((MachineDoubleBase)self, (object)input)	Executes the machine on the given 1D numpy array of float64, and returns the output.
`get_closest_mean`((KMeansMachine)self, (object)x)	Calculate the index of the mean that is closest (in terms of square Euclidean distance) to the data sample, x
`get_distance_from_mean`((KMeansMachine)self, …)	Return the power of two of the square Euclidean distance of the sample, x, to the i’th mean
`get_mean`((KMeansMachine)self, (int)i)	Get the i’th mean
`get_min_distance`((KMeansMachine)self, …)	Output the minimum square Euclidean distance between the input and one of the means
`get_variances_and_weights_for_each_cluster`(…)	For each mean, find the subset of the samples that is closest to that mean, and calculate 1) the variance of that subset (the cluster variance) 2) the proportion of the samples represented by that subset (the cluster weight)
`is_similar_to`((KMeansMachine)self, …)	Compares this KMeansMachine with the ‘other’ one to be approximately the same.
`load`((KMeansMachine)self, (HDF5File)config)	Load from a Configuration
`resize`((KMeansMachine)self, (int)n_means, …)	Resize the number of means and inputs
`save`((KMeansMachine)self, (HDF5File)config)	Save to a Configuration
`set_mean`((KMeansMachine)self, (int)i, …)	Set the i’th mean

Attributes

`dim_c`	Number of means (k)
`dim_d`	Number of inputs
`means`	The mean vectors

__call__((MachineDoubleBase)self, (object)input) → float :¶: Executes the machine on the given 1D numpy array of float64, and returns the output. NO CHECK is performed.

dim_c¶: Number of means (k)

dim_d¶: Number of inputs

forward((MachineDoubleBase)self, (object)input) → float :¶: Executes the machine on the given 1D numpy array of float64, and returns the output.

forward_((MachineDoubleBase)self, (object)input) → float :¶: Executes the machine on the given 1D numpy array of float64, and returns the output. NO CHECK is performed.

get_closest_mean((KMeansMachine)self, (object)x) → tuple :¶: Calculate the index of the mean that is closest (in terms of square Euclidean distance) to the data sample, x

get_distance_from_mean((KMeansMachine)self, (object)x, (int)i) → float :¶: Return the power of two of the square Euclidean distance of the sample, x, to the i’th mean

get_mean((KMeansMachine)self, (int)i) → object :¶: Get the i’th mean

get_min_distance((KMeansMachine)self, (object)input) → float :¶: Output the minimum square Euclidean distance between the input and one of the means

get_variances_and_weights_for_each_cluster((KMeansMachine)self, (object)data) → tuple :¶: For each mean, find the subset of the samples that is closest to that mean, and calculate 1) the variance of that subset (the cluster variance) 2) the proportion of the samples represented by that subset (the cluster weight)

is_similar_to((KMeansMachine)self, (KMeansMachine)other[, (float)r_epsilon=1e-05[, (float)a_epsilon=1e-08]]) → bool :¶: Compares this KMeansMachine with the ‘other’ one to be approximately the same.

load((KMeansMachine)self, (HDF5File)config) → None :¶: Load from a Configuration

means¶: The mean vectors

resize((KMeansMachine)self, (int)n_means, (int)n_inputs) → None :¶: Resize the number of means and inputs

save((KMeansMachine)self, (HDF5File)config) → None :¶: Save to a Configuration

set_mean((KMeansMachine)self, (int)i, (object)mean) → None :¶: Set the i’th mean