示例#1
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def run_single(run_id):
    '''
Given an input dictionary containing a single paramater set
run mcmc in matlab using bs_macros.run_matlab.
'''
    input_dict = butils.load_data(run_id,'input')
    return bsm.runmat('run_mcmc', input_dict, run_id)
示例#2
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def compute_embedding(affinities,
		      aff_type = 'pairs',
		      do_mve = False,
		      ss_multiplier = None):


	mve_dict = dict(k = 4,
			similarities = affinities,
                        do_mve = False if aff_type == 'easy' else False)
        embedding=  bsm.runmat('mve_frompy', mve_dict, 'subopt_mve')
        mve_vecs = embedding['Y'].T
        pca_vecs = embedding['YPCA'].T

	return pca_vecs, mve_vecs
示例#3
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def test_bsubfun(run_id):
    """
A sample function to demonstrate the calling of a matlab script (here, 
ap_frompy) from within python. Taking an input dictionary and a run_id,
this script is designed to be called using the 'eyeball' class from 
utils/bsub.py.

inputs:
  input_dict: {similarities: a similarity matrix for the input points,
               self_similarity: a single value for the self similarity
                                of datapoints. Control cluster size.

outputs:
  outpt_dict: {indexes: cluster exemplar indices.}

"""
    input_dict = butils.load_data(run_id, "input")
    return bsm.runmat("ap_frompy", input_dict, run_id)
示例#4
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def test_bsubfun(run_id):
    '''
A sample function to demonstrate the calling of a matlab script (here, 
ap_frompy) from within python. Taking an input dictionary and a run_id,
this script is designed to be called using the 'eyeball' class from 
utils/bsub.py.

inputs:
  input_dict: {similarities: a similarity matrix for the input points,
               self_similarity: a single value for the self similarity
                                of datapoints. Control cluster size.

outputs:
  outpt_dict: {indexes: cluster exemplar indices.}

'''
    input_dict = butils.load_data(run_id, 'input')
    return bsm.runmat('ap_frompy', input_dict, run_id)
示例#5
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def bic_clustering(run_id):
    """
A matlab/bsub process to compute the BIC maximal clustering for an
input dictionary containing a similarity matrix.

inputs:
  input_dict:  {similarities: a similarity matrix}

outputs:
  output_dict: {inds:cluster exemplar indices,      (MAX BIC)
                self_similarity:float, self similarity (MAX BIC)
                
                inds_[#]: (same as above, ALL BIC)
                self_similarity_[#}: (...)
                bic_[#]: (...)
                }
"""
    input_dict = butils.load_data(run_id, "input")
    return bsm.runmat("ap_max_bic", input_dict, run_id)
示例#6
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def bic_clustering(run_id):
    '''
A matlab/bsub process to compute the BIC maximal clustering for an
input dictionary containing a similarity matrix.

inputs:
  input_dict:  {similarities: a similarity matrix}

outputs:
  output_dict: {inds:cluster exemplar indices,      (MAX BIC)
                self_similarity:float, self similarity (MAX BIC)
                
                inds_[#]: (same as above, ALL BIC)
                self_similarity_[#}: (...)
                bic_[#]: (...)
                }
'''
    input_dict = butils.load_data(run_id, 'input')
    return bsm.runmat('ap_max_bic', input_dict, run_id)
示例#7
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def compute_clusters(affinities, ss):
        cluster_dict = dict(similarities=affinities,
                           self_similarity = ss)
        clusters =  bsm.runmat('ap_frompy', cluster_dict, 'subopt_clusters')
        return squeeze(clusters['inds'])