Python numpy.unravel_index方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def large_ci(ci, norb, nelec, tol=LARGE_CI_TOL, return_strs=RETURN_STRS):
    '''Search for the largest CI coefficients
    '''
    neleca, nelecb = _unpack_nelec(nelec)
    na = cistring.num_strings(norb, neleca)
    nb = cistring.num_strings(norb, nelecb)
    assert(ci.shape == (na, nb))
    addra, addrb = numpy.where(abs(ci) > tol)
    if addra.size == 0:
        # No large CI coefficient > tol, search for the largest coefficient
        addra, addrb = numpy.unravel_index(numpy.argmax(abs(ci)), ci.shape)
        addra = numpy.asarray([addra])
        addrb = numpy.asarray([addrb])
    strsa = cistring.addrs2str(norb, neleca, addra)
    strsb = cistring.addrs2str(norb, nelecb, addrb)
    if return_strs:
        strsa = [bin(x) for x in strsa]
        strsb = [bin(x) for x in strsb]
        return list(zip(ci[addra,addrb], strsa, strsb))
    else:
        occslsta = cistring._strs2occslst(strsa, norb)
        occslstb = cistring._strs2occslst(strsb, norb)
        return list(zip(ci[addra,addrb], occslsta, occslstb)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def nonin_osc_strength(self):
    from scipy.sparse import spmatrix 
    """ Computes the non-interacting oscillator strengths and energies """

    x,y,z = map(spmatrix.toarray, self.dipole_coo())
    i2d = array((x,y,z))
    n = self.mo_occ.shape[-1]
    
    p = zeros((len(comega)), dtype=np.complex128) # result to accumulate
    
    for s in range(self.nspin):
      o,e,cc = self.mo_occ[0,s],self.mo_energy[0,s],self.mo_coeff[0,s,:,:,0]
      oo1,ee1 = np.subtract.outer(o,o).reshape(n*n), np.subtract.outer(e,e).reshape(n*n)
      idx = unravel_index( np.intersect1d(where(oo1<0.0), where(ee1<eemax)), (n,n))
      ivrt,iocc = array(list(set(idx[0]))), array(list(set(idx[1])))
      voi2d = einsum('nia,ma->nmi', einsum('iab,nb->nia', i2d, cc[ivrt]), cc[iocc])
      t2osc = 2.0/3.0*einsum('voi,voi->vo', voi2d, voi2d)
      t2w =  np.subtract.outer(e[ivrt],e[iocc])
      t2o = -np.subtract.outer(o[ivrt],o[iocc])

      for iw,w in enumerate(comega):
        p[iw] += 0.5*(t2osc*((t2o/(w-t2w))-(t2o/(w+t2w)))).sum()      
    return p 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def select_merge_data(self, u_feas, label, label_to_images,  ratio_n,  dists):
        dists.add_(torch.tril(100000 * torch.ones(len(u_feas), len(u_feas))))#blocking the triangle

        cnt = torch.FloatTensor([len(label_to_images[label[idx]]) for idx in range(len(u_feas))])
        dists += ratio_n * (cnt.view(1, len(cnt)) + cnt.view(len(cnt), 1))  # dist += |A|+|B|
        
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if label[idx] == label[j]:
                    dists[idx, j] = 100000                  # set the distance within the same cluster

        dists = dists.numpy()
        ind = np.unravel_index(np.argsort(dists, axis=None), dists.shape)          # with axis=None all numbers are sorted and unravel_index transforms the sorted index into ind for each dimension
        idx1 = ind[0]          # the first dimension index
        idx2 = ind[1]           # the second dimension index
        return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def select_merge_data_v2(self, u_feas, labels, linkages):
        linkages+=(np.tril(100000 * np.ones((len(u_feas), len(u_feas)))))  # blocking the triangle

        print('Linkage adding')
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if labels[idx] == labels[j]:
                    linkages[idx, j] = 100000  # set the distance within the same cluster

        ind = np.unravel_index(np.argsort(linkages, axis=None),
                               linkages.shape)  # with axis=None all numbers are sorted and unravel_index transforms the sorted index into ind for each dimension
        idx1 = ind[0]  # the first cluster index
        idx2 = ind[1]  # the second cluster index
        print('Linkage add finished')
        return idx1, idx2


        #after 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def select_merge_data(self, u_feas, label, label_to_images,  ratio_n,  dists):
        dists.add_(torch.tril(100000 * torch.ones(len(u_feas), len(u_feas))))

        cnt = torch.FloatTensor([len(label_to_images[label[idx]]) for idx in range(len(u_feas))])
        dists += ratio_n * (cnt.view(1, len(cnt)) + cnt.view(len(cnt), 1))  
        
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if label[idx] == label[j]:
                    dists[idx, j] = 100000                 

        dists = dists.numpy()
        ind = np.unravel_index(np.argsort(dists, axis=None), dists.shape)          
        idx1 = ind[0]          
        idx2 = ind[1]          
        return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def offset_to_index(self, index, offset):
    """Calculate the index of another box at offset w.r.t.

    current index.

    Args:
      index: the current flat index from which to calculate the offset index.
      offset: the xyz offset from current index at which to calculate the new
        index.

    Returns:
      The flat index at offset from current index, or None if the given offset
      goes beyond the range of sub-boxes.

    This is usually used to calculate the boxes that neighbor the current box.
    """
    coords = np.unravel_index(index, self.total_sub_boxes_xyz, order='F')
    offset_coords = np.array(coords) + offset
    if np.any(offset_coords < 0) or np.any(
        offset_coords >= self.total_sub_boxes_xyz):
      return None
    return np.ravel_multi_index(
        offset_coords, self.total_sub_boxes_xyz, order='F') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def tag_border_locations(self, index):
    """Checks whether a box touches the border of the BoundingBox.

    Args:
      index: flat index identifying the box to check

    Returns:
      2-tuple of bool 3d ndarrays (dim order: x, y, z).
      True if the box touches the border at the start/end (respectively for the
      1st and 2nd element of the tuple) of the bbox along the given dimension.
    """
    coords_xyz = np.array(
        np.unravel_index(index, self.total_sub_boxes_xyz, order='F'))
    is_start = coords_xyz == 0
    is_end = coords_xyz == self.total_sub_boxes_xyz - 1
    return is_start, is_end 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def __init__(self):
        self.shape = (4, 12)

        nS = np.prod(self.shape)
        nA = 4

        # Cliff Location
        self._cliff = np.zeros(self.shape, dtype=np.bool)
        self._cliff[3, 1:-1] = True

        # Calculate transition probabilities
        P = {}
        for s in range(nS):
            position = np.unravel_index(s, self.shape)
            P[s] = { a : [] for a in range(nA) }
            P[s][UP] = self._calculate_transition_prob(position, [-1, 0])
            P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1])
            P[s][DOWN] = self._calculate_transition_prob(position, [1, 0])
            P[s][LEFT] = self._calculate_transition_prob(position, [0, -1])

        # We always start in state (3, 0)
        isd = np.zeros(nS)
        isd[np.ravel_multi_index((3,0), self.shape)] = 1.0

        super(CliffWalkingEnv, self).__init__(nS, nA, P, isd) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def _render(self, mode='human', close=False):
        if close:
            return

        outfile = StringIO() if mode == 'ansi' else sys.stdout

        for s in range(self.nS):
            position = np.unravel_index(s, self.shape)
            # print(self.s)
            if self.s == s:
                output = " x "
            elif position == (3,7):
                output = " T "
            else:
                output = " o "

            if position[1] == 0:
                output = output.lstrip()
            if position[1] == self.shape[1] - 1:
                output = output.rstrip()
                output += "\n"

            outfile.write(output)
        outfile.write("\n") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def bbox_associate(overlap_mat, IOU_thresh): 
    idx1 = [] 
    idx2 = [] 
    new_overlap_mat = overlap_mat.copy()
    while 1: 
        idx = np.unravel_index(np.argmax(new_overlap_mat, axis=None), new_overlap_mat.shape) 
        if new_overlap_mat[idx]<IOU_thresh: 
            break 
        else: 
            idx1.append(idx[0]) 
            idx2.append(idx[1]) 
            new_overlap_mat[idx[0],:] = 0 
            new_overlap_mat[:,idx[1]] = 0

    idx1 = np.array(idx1)
    idx2 = np.array(idx2)
    return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def bbox_associate(overlap_mat, IOU_thresh): 
    idx1 = [] 
    idx2 = [] 
    while 1: 
        idx = np.unravel_index(np.argmax(overlap_mat, axis=None), overlap_mat.shape) 
        if overlap_mat[idx]<IOU_thresh: 
            break 
        else: 
            idx1.append(idx[0]) 
            idx2.append(idx[1]) 
            overlap_mat[idx[0],:] = 0 
            overlap_mat[:,idx[1]] = 0

    idx1 = np.array(idx1)
    idx2 = np.array(idx2)
    return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def reshape(self, shape, order='C'):
        if type(order) != str or order != 'C':
            raise ValueError(("Sparse matrices do not support "
                              "an 'order' parameter."))

        if type(shape) != tuple:
            raise TypeError("a tuple must be passed in for 'shape'")

        if len(shape) != 2:
            raise ValueError("a length-2 tuple must be passed in for 'shape'")

        new = lil_matrix(shape, dtype=self.dtype)
        j_max = self.shape[1]

        # Size is ambiguous for sparse matrices, so in order to check 'total
        # dimension', we need to take the product of their dimensions instead
        if new.shape[0] * new.shape[1] != self.shape[0] * self.shape[1]:
            raise ValueError("the product of the dimensions for the new sparse "
                             "matrix must equal that of the original matrix")

        for i, row in enumerate(self.rows):
            for col, j in enumerate(row):
                new_r, new_c = np.unravel_index(i*j_max + j, shape)
                new[new_r, new_c] = self[i, j]
        return new 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def Sampling(heatmap, K):
    # heatmap: [n,h,w]
    # return: [n,K,2]
    heatmap = np.exp(-heatmap/2)
    n,h,w=heatmap.shape
    pt = np.zeros([n,K,2])
    WINDOW_SZ = 15
    for i in range(n):
        for j in range(K):
            idx=np.argmax(heatmap[i])
            coord=np.unravel_index(idx,heatmap[i].shape)[::-1]
            pt[i,j,:]=coord
            # suppress the neighbors
            topl=[max(0,coord[0] - WINDOW_SZ),max(0,coord[1] - WINDOW_SZ)]
            botr=[min(w-1,coord[0] + WINDOW_SZ),min(h-1,coord[1] + WINDOW_SZ)]
            heatmap[i][topl[1]:botr[1],topl[0]:botr[0]] = heatmap[i].min()
    return pt 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def render(self, mode='human'):
        outfile = sys.stdout

        for s in range(self.nS):
            position = np.unravel_index(s, self.shape)
            if self.s == s:
                output = " x "
            # Print terminal state
            elif position == (3, 11):
                output = " T "
            elif self._cliff[position]:
                output = " C "
            else:
                output = " o "

            if position[1] == 0:
                output = output.lstrip()
            if position[1] == self.shape[1] - 1:
                output = output.rstrip()
                output += '\n'

            outfile.write(output)
        outfile.write('\n') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import unravel_index [as 別名]
def curve_intersection(c1, c2, grid=16):
    '''
    curve_intersect(c1, c2) yields the parametric distances (t1, t2) such that c1(t1) == c2(t2).
      
    The optional parameter grid may specify the number of grid-points
    to use in the initial search for a start-point (default: 16).
    '''
    from scipy.optimize import minimize
    from neuropythy.geometry import segment_intersection_2D
    if c1.coordinates.shape[1] > c2.coordinates.shape[1]:
        (t1,t2) = curve_intersection(c2, c1, grid=grid)
        return (t2,t1)
    # before doing a search, see if there are literal exact intersections of the segments
    x1s  = c1.coordinates.T
    x2s  = c2.coordinates
    for (ts,te,xs,xe) in zip(c1.t[:-1], c1.t[1:], x1s[:-1], x1s[1:]):
        pts = segment_intersection_2D((xs,xe), (x2s[:,:-1], x2s[:,1:]))
        ii = np.where(np.isfinite(pts[0]))[0]
        if len(ii) > 0:
            ii = ii[0]
            def f(t): return np.sum((c1(t[0]) - c2(t[1]))**2)
            t01 = 0.5*(ts + te)
            t02 = 0.5*(c2.t[ii] + c2.t[ii+1])
            (t1,t2) = minimize(f, (t01, t02)).x
            return (t1,t2)
    if pimms.is_vector(grid): (ts1,ts2) = [c.t[0] + (c.t[-1] - c.t[0])*grid for c in (c1,c2)]
    else:                     (ts1,ts2) = [np.linspace(c.t[0], c.t[-1], grid) for c in (c1,c2)]
    (pts1,pts2) = [c(ts) for (c,ts) in zip([c1,c2],[ts1,ts2])]
    ds = np.sqrt([np.sum((pts2.T - pp)**2, axis=1) for pp in pts1.T])
    (ii,jj) = np.unravel_index(np.argmin(ds), ds.shape)
    (t01,t02) = (ts1[ii], ts2[jj])
    ttt = []
    def f(t): return np.sum((c1(t[0]) - c2(t[1]))**2)
    (t1,t2) = minimize(f, (t01, t02)).x
    return (t1,t2)