Python numpy.count_nonzero方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _accuracy(self, y_test, Y_vote):
        """Calculates accuracy

            This method calculates the accuracy based on a vector of
            ground-truth labels (y_test) and a 2D voting matrix (Y_vote) of
            size (len(y_test), num_classes).

            :param y_test: vector of ground-truth labels
            :param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
            :returns: accuracy e[0,1]
        """
        # predicted classes
        y_hat = np.argmax(Y_vote, axis=1)

        # all cases where predicted class was correct
        mask = y_hat == y_test
        return np.float32(np.count_nonzero(mask)) / len(y_test) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _confusion(self, y_test, Y_vote):
        """Calculates confusion matrix

            This method calculates the confusion matrix based on a vector of
            ground-truth labels (y-test) and a 2D voting matrix (Y_vote) of
            size (len(y_test), num_classes).
            Matrix element conf[r,c] will contain the number of samples that
            were predicted to have label r but have ground-truth label c.

            :param y_test: vector of ground-truth labels
            :param Y_vote: 2D voting matrix (rows=samples, cols=class votes)
            :returns: confusion matrix
        """
        y_hat = np.argmax(Y_vote, axis=1)
        conf = np.zeros((self.num_classes, self.num_classes)).astype(np.int32)
        for c_true in xrange(self.num_classes):
            # looking at all samples of a given class, c_true
            # how many were classified as c_true? how many as others?
            for c_pred in xrange(self.num_classes):
                y_this = np.where((y_test == c_true) * (y_hat == c_pred))
                conf[c_pred, c_true] = np.count_nonzero(y_this)
        return conf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def test_gen_mass(self):
        r"""Test gen_mass method.

        Approach: Ensures the output is set, of the correct type, length, and units.
        Check the range of the returned values.  Check that, for this power law, there
        are more small than large masses (for n large).
        """

        plan_pop = self.fixture
        n = 10000
        # call the routine
        masses = plan_pop.gen_mass(n)
        # check the type
        self.assertEqual(type(masses), type(1.0 * u.kg))
        # crude check on the shape (more small than large for this power law)
        midpoint = np.mean(plan_pop.Mprange)
        self.assertGreater(np.count_nonzero(masses < midpoint),
                           np.count_nonzero(masses > midpoint))
        # test some illegal "n" values
        n_list_bad = [-1, '100', 22.5]
        for n in n_list_bad:
            with self.assertRaises(AssertionError):
                masses = plan_pop.gen_mass(n) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def test_gen_mass(self):
        r"""Test gen_mass method.

        Approach: Ensures the output is set, of the correct type, length, and units.
        Check that returned values are nonnegative.  Check that, for this power law, there
        are more small than large masses (for n large).
        """

        plan_pop = self.fixture
        n = 10000
        masses = plan_pop.gen_mass(n)

        self.assertEqual(len(masses), n)
        self.assertTrue(np.all(masses.value >= 0))
        self.assertTrue(np.all(np.isfinite(masses.value)))

        midpoint = np.mean(masses)
        self.assertGreater(np.count_nonzero(masses < midpoint),
                           np.count_nonzero(masses > midpoint))

        # test some illegal "n" values
        n_list_bad = [-1, '100', 22.5]
        for n in n_list_bad:
            with self.assertRaises(AssertionError):
                masses = plan_pop.gen_mass(n) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def test_init_indexes(self):
        r"""Test of initialization and __init__ -- indexes.

        Method: Insure the plan2star and sInds indexes are present. 
        Performs sanity check on the range of index values.
        TODO: More could be done to ensure the index values are correct.
        """
        universe = self.fixture
        self.basic_validation(universe)
        # indexes present
        self.assertIn('plan2star', universe.__dict__)
        self.assertIn('sInds', universe.__dict__)
        # range: 0 <= sInds < nStars
        self.assertEqual(0, np.count_nonzero(universe.sInds < 0))
        self.assertEqual(0, np.count_nonzero(universe.sInds >= universe.TargetList.nStars))
        # domain: plan2star covers 0...nPlans-1
        self.assertEqual(len(universe.plan2star), universe.nPlans)
        # range: 0 <= plan2star < nStars
        self.assertEqual(0, np.count_nonzero(universe.plan2star < 0))
        self.assertEqual(0, np.count_nonzero(universe.plan2star >= universe.TargetList.nStars)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def canonicalize(mf, mo_coeff_kpts, mo_occ_kpts, fock=None):
    if fock is None:
        dm = mf.make_rdm1(mo_coeff_kpts, mo_occ_kpts)
        fock = mf.get_fock(dm=dm)
    mo_coeff = []
    mo_energy = []
    for k, mo in enumerate(mo_coeff_kpts):
        mo1 = np.empty_like(mo)
        mo_e = np.empty_like(mo_occ_kpts[k])
        occidx = mo_occ_kpts[k] == 2
        viridx = ~occidx
        for idx in (occidx, viridx):
            if np.count_nonzero(idx) > 0:
                orb = mo[:,idx]
                f1 = reduce(np.dot, (orb.T.conj(), fock[k], orb))
                e, c = scipy.linalg.eigh(f1)
                mo1[:,idx] = np.dot(orb, c)
                mo_e[idx] = e
        mo_coeff.append(mo1)
        mo_energy.append(mo_e)
    return mo_energy, mo_coeff 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _frozen_sanity_check(frozen, mo_occ, kpt_idx):
    '''Performs a few sanity checks on the frozen array and mo_occ.

    Specific tests include checking for duplicates within the frozen array.

    Args:
        frozen (array_like of int): The orbital indices that will be frozen.
        mo_occ (:obj:`ndarray` of int): The occupuation number for each orbital
            resulting from a mean-field-like calculation.
        kpt_idx (int): The k-point that `mo_occ` and `frozen` belong to.

    '''
    frozen = np.array(frozen)
    nocc = np.count_nonzero(mo_occ > 0)

    assert nocc, 'No occupied orbitals?\n\nnocc = %s\nmo_occ = %s' % (nocc, mo_occ)
    all_frozen_unique = (len(frozen) - len(np.unique(frozen))) == 0
    if not all_frozen_unique:
        raise RuntimeError('Frozen orbital list contains duplicates!\n\nkpt_idx %s\n'
                           'frozen %s' % (kpt_idx, frozen))
    if len(frozen) > 0 and np.max(frozen) > len(mo_occ) - 1:
        raise RuntimeError('Freezing orbital not in MO list!\n\nkpt_idx %s\n'
                           'frozen %s\nmax orbital idx %s' % (kpt_idx, frozen, len(mo_occ) - 1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _unpack(vo, mo_occ):
    za = []
    zb = []
    p1 = 0
    for k, occ in enumerate(mo_occ[0]):
        no = numpy.count_nonzero(occ > 0)
        nv = occ.size - no
        p0, p1 = p1, p1 + no * nv
        za.append(vo[p0:p1].reshape(no,nv))

    for k, occ in enumerate(mo_occ[1]):
        no = numpy.count_nonzero(occ > 0)
        nv = occ.size - no
        p0, p1 = p1, p1 + no * nv
        zb.append(vo[p0:p1].reshape(no,nv))
    return za, zb 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def canonicalize(mf, mo_coeff, mo_occ, fock=None):
    '''Canonicalization diagonalizes the Fock matrix within occupied, open,
    virtual subspaces separatedly (without change occupancy).
    '''
    if fock is None:
        dm = mf.make_rdm1(mo_coeff, mo_occ)
        fock = mf.get_fock(dm=dm)
    coreidx = mo_occ == 2
    viridx = mo_occ == 0
    openidx = ~(coreidx | viridx)
    mo = numpy.empty_like(mo_coeff)
    mo_e = numpy.empty(mo_occ.size)
    for idx in (coreidx, openidx, viridx):
        if numpy.count_nonzero(idx) > 0:
            orb = mo_coeff[:,idx]
            f1 = reduce(numpy.dot, (orb.conj().T, fock, orb))
            e, c = scipy.linalg.eigh(f1)
            mo[:,idx] = numpy.dot(orb, c)
            mo_e[idx] = e
    return mo_e, mo 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _dump_mo_energy(mol, mo_energy, mo_occ, ehomo, elumo, orbsym, title='',
                    verbose=logger.DEBUG):
    log = logger.new_logger(mol, verbose)
    for i, ir in enumerate(mol.irrep_id):
        irname = mol.irrep_name[i]
        ir_idx = (orbsym == ir)
        nso = numpy.count_nonzero(ir_idx)
        nocc = numpy.count_nonzero(mo_occ[ir_idx])
        e_ir = mo_energy[ir_idx]
        if nocc == 0:
            log.debug('%s%s nocc = 0', title, irname)
        elif nocc == nso:
            log.debug('%s%s nocc = %d  HOMO = %.15g',
                      title, irname, nocc, e_ir[nocc-1])
        else:
            log.debug('%s%s nocc = %d  HOMO = %.15g  LUMO = %.15g',
                      title, irname, nocc, e_ir[nocc-1], e_ir[nocc])
            if e_ir[nocc-1]+1e-3 > elumo:
                log.warn('%s%s HOMO %.15g > system LUMO %.15g',
                         title, irname, e_ir[nocc-1], elumo)
            if e_ir[nocc] < ehomo+1e-3:
                log.warn('%s%s LUMO %.15g < system HOMO %.15g',
                         title, irname, e_ir[nocc], ehomo)
        log.debug('   mo_energy = %s', e_ir) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def float_occ_(mf):
    '''
    For UHF, allowing the Sz value being changed during SCF iteration.
    Determine occupation of alpha and beta electrons based on energy spectrum
    '''
    from pyscf.scf import uhf
    assert(isinstance(mf, uhf.UHF))
    def get_occ(mo_energy, mo_coeff=None):
        mol = mf.mol
        ee = numpy.sort(numpy.hstack(mo_energy))
        n_a = numpy.count_nonzero(mo_energy[0]<(ee[mol.nelectron-1]+1e-3))
        n_b = mol.nelectron - n_a
        if mf.nelec is None:
            nelec = mf.mol.nelec
        else:
            nelec = mf.nelec
        if n_a != nelec[0]:
            logger.info(mf, 'change num. alpha/beta electrons '
                        ' %d / %d -> %d / %d',
                        nelec[0], nelec[1], n_a, n_b)
            mf.nelec = (n_a, n_b)
        return uhf.UHF.get_occ(mf, mo_energy, mo_coeff)
    mf.get_occ = get_occ
    return mf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def test_uniq_var(self):
        mo_occ = mf.mo_occ.copy()
        nmo = mo_occ.size
        nocc = numpy.count_nonzero(mo_occ > 0)
        nvir = nmo - nocc
        numpy.random.seed(1)
        f = numpy.random.random((nmo,nmo))
        f_uniq = scf.hf.pack_uniq_var(f, mo_occ)
        self.assertEqual(f_uniq.size, nocc*nvir)
        f1 = scf.hf.unpack_uniq_var(f_uniq, mo_occ)
        self.assertAlmostEqual(abs(f1 + f1.T).max(), 0, 12)

        mo_occ[4:7] = 1
        ndocc = 4
        nocc = 7
        f_uniq = scf.hf.pack_uniq_var(f, mo_occ)
        self.assertEqual(f_uniq.size, nocc*(nmo-ndocc)-(nocc-ndocc)**2)

        f1 = scf.hf.unpack_uniq_var(f_uniq, mo_occ)
        self.assertAlmostEqual(abs(f1 + f1.T).max(), 0, 12) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def get_nocc(mp):
    if mp._nocc is not None:
        return mp._nocc
    elif mp.frozen is None:
        nocc = numpy.count_nonzero(mp.mo_occ > 0)
        assert(nocc > 0)
        return nocc
    elif isinstance(mp.frozen, (int, numpy.integer)):
        nocc = numpy.count_nonzero(mp.mo_occ > 0) - mp.frozen
        assert(nocc > 0)
        return nocc
    elif isinstance(mp.frozen[0], (int, numpy.integer)):
        occ_idx = mp.mo_occ > 0
        occ_idx[list(mp.frozen)] = False
        nocc = numpy.count_nonzero(occ_idx)
        assert(nocc > 0)
        return nocc
    else:
        raise NotImplementedError 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def pick_real_eigs(w, v, nroots, envs):
    '''This function searchs the real eigenvalues or eigenvalues with small
    imaginary component.
    '''
    threshold = 1e-3
    abs_imag = abs(w.imag)
    # Grab `nroots` number of e with small(est) imaginary components
    max_imag_tol = max(threshold, numpy.sort(abs_imag)[min(w.size,nroots)-1])
    real_idx = numpy.where((abs_imag <= max_imag_tol))[0]
    nbelow_thresh = numpy.count_nonzero(abs_imag[real_idx] < threshold)
    if nbelow_thresh < nroots and w.size >= nroots:
        warnings.warn('Only %d eigenvalues (out of %3d requested roots) with imaginary part < %4.3g.\n'
                      % (nbelow_thresh, min(w.size,nroots), threshold))

    # Guess whether the matrix to diagonalize is real or complex
    if envs.get('dtype') == numpy.double:
        w, v, idx = _eigs_cmplx2real(w, v, real_idx, real_eigenvectors=True)
    else:
        w, v, idx = _eigs_cmplx2real(w, v, real_idx, real_eigenvectors=False)
    return w, v, idx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import count_nonzero [as 別名]
def _sort_by_similarity(w, v, nroots, conv, vlast, emin=None, heff=None):
    if not any(conv) or vlast is None:
        return w[:nroots], v[:,:nroots]

    head, nroots = vlast.shape
    conv = numpy.asarray(conv[:nroots])
    ovlp = vlast[:,conv].T.conj().dot(v[:head])
    ovlp = numpy.einsum('ij,ij->j', ovlp, ovlp)
    nconv = numpy.count_nonzero(conv)
    nleft = nroots - nconv
    idx = ovlp.argsort()
    sorted_idx = numpy.zeros(nroots, dtype=int)
    sorted_idx[conv] = numpy.sort(idx[-nconv:])
    sorted_idx[~conv] = numpy.sort(idx[:-nconv])[:nleft]

    e = w[sorted_idx]
    c = v[:,sorted_idx]
    return e, c