Python sg.sg函数代码示例

def multiplicity(hkls, sgname=None, sgno=None, cell_choice="standard"):
    """
    Calculate the powder diffraction multiplicity of a set of reflections
	

    INPUT:  hkls          : HKLs for the reflections
            sgno/sgname   : provide either the space group number or its name 
                            e.g. sgno=225 or equivalently
                                 sgname='Fm-3m'

    OUTPUT: array of multiplicities

    """

    if sgname != None:
        spg = sg.sg(sgname=sgname, cell_choice=cell_choice)
    elif sgno != None:
        spg = sg.sg(sgno=sgno, cell_choice=cell_choice)
    else:
        raise ValueError, "No space group information given"

    # Making sure that the inversion element also for non-centrosymmetric space groups
    Rots = np.concatenate((spg.rot[: spg.nuniq], -spg.rot[: spg.nuniq]))
    (dummy, rows) = np.unique((Rots * np.random.rand(3, 3)).sum(axis=2).sum(axis=1), return_index=True)
    Rots = Rots[np.sort(rows)]

    M = []
    for refl in hkls:
        a = np.array([np.dot(refl[:3], R) for R in Rots])

        (dummy, rows) = np.unique((a * np.random.rand(3)).sum(axis=1), return_index=True)

        M.append(a[rows].shape[0])
    return np.array(M)

def _get_alpha_and_sv(indata, prefix):
	if not indata.has_key(prefix+'alpha_sum') and \
		not indata.has_key(prefix+'sv_sum'):
		return None, None

	a=0
	sv=0
	if indata.has_key(prefix+'label_type') and \
		indata[prefix+'label_type']=='series':
		for i in xrange(sg('get_num_svms')):
			[dump, weights]=sg('get_svm', i)
			weights=weights.T
			for item in weights[0].tolist():
				a+=item
			for item in weights[1].tolist():
				sv+=item
		a=abs(a-indata[prefix+'alpha_sum'])
		sv=abs(sv-indata[prefix+'sv_sum'])
	else:
		[dump, weights]=sg('get_svm')
		weights=weights.T
		for item in weights[0].tolist():
			a+=item
		a=abs(a-indata[prefix+'alpha_sum'])
		for item in weights[1].tolist():
			sv+=item
		sv=abs(sv-indata[prefix+'sv_sum'])

	return a, sv

def multiplicity(position, sgname=None, sgno=None, cell_choice='standard'):
    """
    Calculates the multiplicity of a fractional position in the unit cell.
    If called by sgno, cell_choice is necessary for eg rhombohedral space groups.

    """

    if sgname != None:
        mysg = sg.sg(sgname=sgname, cell_choice=cell_choice)
    elif sgno !=None:
        mysg = sg.sg(sgno=sgno, cell_choice=cell_choice)
    else:
        raise ValueError, 'No space group information provided'

    lp = n.zeros((mysg.nsymop, 3))

    for i in range(mysg.nsymop):
        lp[i, :] = n.dot(position, mysg.rot[i]) + mysg.trans[i]

    lpu = n.array([lp[0, :]])
    multi = 1

    for i in range(1, mysg.nsymop):
        for j in range(multi):
            t = lp[i]-lpu[j]
            if n.sum(n.mod(t, 1)) < 0.00001:
                break
            else:
                if j == multi-1:
                    lpu = n.concatenate((lpu, [lp[i, :]]))
                    multi += 1
    return multi

def _set_distribution (indata):
	prefix='distribution_'
	if indata[prefix+'name']=='HMM':
		sg('new_hmm', indata[prefix+'N'], indata[prefix+'M'])
		sg('bw')
	else:
		raise NotImplementedError, 'Can\'t yet train other distributions than HMM in static interface.'

def _evaluate (indata, prefix):
	dmatrix=sg('get_distance_matrix', 'TRAIN')
	dm_train=max(abs(indata['distance_matrix_train']-dmatrix).flat)

	dmatrix=sg('get_distance_matrix', 'TEST')
	dm_test=max(abs(indata['distance_matrix_test']-dmatrix).flat)

	return util.check_accuracy(
		indata[prefix+'accuracy'], dm_train=dm_train, dm_test=dm_test)

def _train (indata):
	if indata['regression_type']=='svm':
		sg('c', double(indata['regression_C']))
		sg('svm_epsilon', indata['regression_epsilon'])
		sg('svr_tube_epsilon', indata['regression_tube_epsilon'])
	elif indata['regression_type']=='kernelmachine':
		sg('krr_tau', indata['regression_tau'])
	else:
		raise StandardError, 'Incomplete regression data.'

	sg('train_regression')

    def predict(self, testPoints):
        """Predicts performance using previously learned model.
        self.train() must be called before this!"""

        if len(testPoints.shape) < 2:
            testPoints = array([testPoints])

        sg("set_features", "TEST", phys2unif(testPoints, self.ranges).T)
        predictions = sg("classify")

        return predictions

    def predict(self, testPoints):
        '''Predicts performance using previously learned model.
        self.train() must be called before this!'''

        if len(testPoints.shape) < 2:
            testPoints = array([testPoints])

        sg('set_features', 'TEST', phys2unif(testPoints,self.ranges).T)
        predictions = sg('classify')

        return predictions

def _evaluate (indata, prefix):
	util.set_and_train_kernel(indata)

	kmatrix=sg('get_kernel_matrix', 'TRAIN')
	km_train=max(abs(indata['kernel_matrix_train']-kmatrix).flat)

	kmatrix=sg('get_kernel_matrix', 'TEST')
	km_test=max(abs(indata['kernel_matrix_test']-kmatrix).flat)

	return util.check_accuracy(
		indata[prefix+'accuracy'], km_train=km_train, km_test=km_test)

def clustering_kmeans (fm_train=traindat, size_cache=10,k=3,iter=1000):
	sg('set_features', 'TRAIN', fm_train)
	sg('set_distance', 'EUCLIDIAN', 'REAL')
	sg('new_clustering', 'KMEANS')
	sg('train_clustering', k, iter)

	[radi, centers]=sg('get_clustering')
	return [radi, centers]

def clustering_hierarchical (fm_train=traindat, size_cache=10,merges=3):

	sg('set_features', 'TRAIN', fm_train)
	sg('set_distance', 'EUCLIDIAN', 'REAL')
	sg('new_clustering', 'HIERARCHICAL')
	sg('train_clustering', merges)

	[merge_distance, pairs]=sg('get_clustering')
	return [merge_distance, pairs]

def kernel_const(fm_train_real=traindat, fm_test_real=testdat, c=23.0, size_cache=10):
    sg("set_features", "TRAIN", fm_train_real)
    sg("set_features", "TEST", fm_test_real)
    sg("set_kernel", "CONST", "REAL", size_cache, c)
    km = sg("get_kernel_matrix", "TRAIN")
    km = sg("get_kernel_matrix", "TEST")
    return km

def distance_chisquare (fm_train_real=traindat,fm_test_real=testdat):
	sg('set_distance', 'CHISQUARE', 'REAL')
	sg('set_features', 'TRAIN', fm_train_real)
	dm=sg('get_distance_matrix', 'TRAIN')
	sg('set_features', 'TEST', fm_test_real)
	dm=sg('get_distance_matrix', 'TEST')
	return dm

def distance_braycurtis (fm_train_real=traindat,fm_test_real=testdat):
	sg('set_distance', 'BRAYCURTIS', 'REAL')
	sg('set_features', 'TRAIN', fm_train_real)
	dm=sg('get_distance_matrix', 'TRAIN')
	sg('set_features', 'TEST', fm_test_real)
	dm=sg('get_distance_matrix', 'TEST')
	return dm

def distance_cosine (fm_train_real=traindat,fm_test_real=testdat):
	sg('set_distance', 'COSINE', 'REAL')
	sg('set_features', 'TRAIN', fm_train_real)
	dm=sg('get_distance_matrix', 'TRAIN')
	sg('set_features', 'TEST', fm_test_real)
	dm=sg('get_distance_matrix', 'TEST')
	return dm