Python numpy.argmin方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def find_match(self, pred, gt):
    '''
    Match component to balls.
    '''
    batch_size, n_frames_input, n_components, _ = pred.shape
    diff = pred.reshape(batch_size, n_frames_input, n_components, 1, 2) - \
               gt.reshape(batch_size, n_frames_input, 1, n_components, 2)
    diff = np.sum(np.sum(diff ** 2, axis=-1), axis=1)
    # Direct indices
    indices = np.argmin(diff, axis=2)
    ambiguous = np.zeros(batch_size, dtype=np.int8)
    for i in range(batch_size):
      _, counts = np.unique(indices[i], return_counts=True)
      if not np.all(counts == 1):
        ambiguous[i] = 1
    return indices, ambiguous 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def _extract(self, intervals, out, **kwargs):

        def find_closest(ldm, interval, use_strand=True):
            """Uses
            """
            # subset the positions to the appropriate strand
            # and extract the positions
            ldm_positions = ldm.loc[interval.chrom]
            if use_strand and interval.strand != ".":
                ldm_positions = ldm_positions.loc[interval.strand]
            ldm_positions = ldm_positions.position.values

            int_midpoint = (interval.end + interval.start) // 2
            dist = (ldm_positions - 1) - int_midpoint  # -1 for 0, 1 indexed positions
            if use_strand and interval.strand == "-":
                dist = - dist

            return dist[np.argmin(np.abs(dist))]

        out[:] = np.array([[find_closest(self.landmarks[ldm_name], interval, self.use_strand)
                            for ldm_name in self.columns]
                           for interval in intervals], dtype=float)

        return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def whichTimeComesNext(self, absTs):
        """ Determine which absolute time comes next from current time
        Specifically designed to determine when the next local zodiacal light event occurs form fZQuads 
        Args:
            absTs (list) - the absolute times of different events (list of absolute times)
        Return:
            absT (astropy time quantity) - the absolute time which occurs next
        """
        TK = self.TimeKeeping
        #Convert Abs Times to norm Time
        tabsTs = list()
        for i in np.arange(len(absTs)):
            tabsTs.append((absTs[i] - TK.missionStart).value) # all should be in first year
        tSinceStartOfThisYear = TK.currentTimeNorm.copy().value%365.25
        if len(tabsTs) == len(np.where(tSinceStartOfThisYear < np.asarray(tabsTs))[0]): # time strictly less than all absTs
            absT = absTs[np.argmin(tabsTs)]
        elif len(tabsTs) == len(np.where(tSinceStartOfThisYear > np.asarray(tabsTs))[0]):
            absT = absTs[np.argmin(tabsTs)]
        else: #Some are above and some are below
            tmptabsTsInds = np.where(tSinceStartOfThisYear < np.asarray(tabsTs))[0]
            absT = absTs[np.argmin(np.asarray(tabsTs)[tmptabsTsInds])] # of times greater than current time, returns smallest

        return absT 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def run(self):
        variables = self.init_variables
        for i in range(self.epochs):
            grads = gradients(self.func,variables)
            if self.func_type == gradients_config.func_type_min:
                variables -= self.lr*grads
            else:
                variables += self.lr*grads
            self.generations_points.append(variables)
            self.generations_targets.append(self.func(variables))

        if self.func_type == gradients_config.func_type_min:
            self.global_best_target = np.min(np.array(self.generations_targets))
            self.global_best_index = np.argmin(np.array(self.generations_targets))
            self.global_best_point = self.generations_points[int(self.global_best_index)]
        else:
            self.global_best_target = np.max(np.array(self.generations_targets))
            self.global_best_index = np.argmax(np.array(self.generations_targets))
            self.global_best_point = self.generations_points[int(self.global_best_index)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def _do(self, F, return_pseudo_weights=False, **kwargs):

        # here the normalized values are ignored but the ideal and nadir points are estimated to calculate trade-off
        _, norm, ideal_point, nadir_point = normalize(F, self.ideal_point, self.nadir_point,
                                                      estimate_bounds_if_none=True, return_bounds=True)

        # normalized distance to the worst solution
        pseudo_weights = ((nadir_point - F) / norm)

        # normalize weights to sum up to one
        pseudo_weights = pseudo_weights / np.sum(pseudo_weights, axis=1)[:, None]

        # search for the closest individual having this pseudo weights
        I = np.argmin(np.sum(np.abs(pseudo_weights - self.weights), axis=1))

        if return_pseudo_weights:
            return I, pseudo_weights
        else:
            return I 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def get_extreme_points_c(F, ideal_point, extreme_points=None):
    # calculate the asf which is used for the extreme point decomposition
    weights = np.eye(F.shape[1])
    weights[weights == 0] = 1e6

    # add the old extreme points to never loose them for normalization
    _F = F
    if extreme_points is not None:
        _F = np.concatenate([extreme_points, _F], axis=0)

    # use __F because we substitute small values to be 0
    __F = _F - ideal_point
    __F[__F < 1e-3] = 0

    # update the extreme points for the normalization having the highest asf value each
    F_asf = np.max(__F * weights[:, None, :], axis=2)

    I = np.argmin(F_asf, axis=1)
    extreme_points = _F[I, :]

    return extreme_points 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def work_balanced_partition(tasks, costs=None):
    if costs is None:
        costs = numpy.ones(tasks)
    if rank == 0:
        segsize = float(sum(costs)) / pool.size
        loads = []
        cum_costs = numpy.cumsum(costs)
        start_id = 0
        for k in range(pool.size):
            stop_id = numpy.argmin(abs(cum_costs - (k+1)*segsize)) + 1
            stop_id = max(stop_id, start_id+1)
            loads.append([start_id,stop_id])
            start_id = stop_id
        comm.bcast(loads)
    else:
        loads = comm.bcast()
    if rank < len(loads):
        start, stop = loads[rank]
        return tasks[start:stop]
    else:
        return tasks[:0] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def ghf_stability(mf, verbose=None):
    log = logger.new_logger(mf, verbose)
    with_symmetry = True
    g, hop, hdiag = newton_ah.gen_g_hop_ghf(mf, mf.mo_coeff, mf.mo_occ,
                                            with_symmetry=with_symmetry)
    hdiag *= 2
    def precond(dx, e, x0):
        hdiagd = hdiag - e
        hdiagd[abs(hdiagd)<1e-8] = 1e-8
        return dx/hdiagd
    def hessian_x(x): # See comments in function rhf_internal
        return hop(x).real * 2

    x0 = numpy.zeros_like(g)
    x0[g!=0] = 1. / hdiag[g!=0]
    if not with_symmetry:  # allow to break point group symmetry
        x0[numpy.argmin(hdiag)] = 1
    e, v = lib.davidson(hessian_x, x0, precond, tol=1e-4, verbose=log)
    if e < -1e-5:
        log.note('GHF wavefunction has an internal instability')
        mo = _rotate_mo(mf.mo_coeff, mf.mo_occ, v)
    else:
        log.note('GHF wavefunction is stable in the internal stability analysis')
        mo = mf.mo_coeff
    return mo 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def rohf_internal(mf, with_symmetry=True, verbose=None):
    log = logger.new_logger(mf, verbose)
    g, hop, hdiag = newton_ah.gen_g_hop_rohf(mf, mf.mo_coeff, mf.mo_occ,
                                             with_symmetry=with_symmetry)
    hdiag *= 2
    def precond(dx, e, x0):
        hdiagd = hdiag - e
        hdiagd[abs(hdiagd)<1e-8] = 1e-8
        return dx/hdiagd
    def hessian_x(x): # See comments in function rhf_internal
        return hop(x).real * 2

    x0 = numpy.zeros_like(g)
    x0[g!=0] = 1. / hdiag[g!=0]
    if not with_symmetry:  # allow to break point group symmetry
        x0[numpy.argmin(hdiag)] = 1
    e, v = lib.davidson(hessian_x, x0, precond, tol=1e-4, verbose=log)
    if e < -1e-5:
        log.note('ROHF wavefunction has an internal instability.')
        mo = _rotate_mo(mf.mo_coeff, mf.mo_occ, v)
    else:
        log.note('ROHF wavefunction is stable in the internal stability analysis')
        mo = mf.mo_coeff
    return mo 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def hard_negative_multilabel(self):
        """Hard Negative Sampling based on multilabel assumption

        Search the negative sample with largest distance (smallest sim)
        with the anchor within self._k negative samplels
        """
        # During early iterations of sampling, use random sampling instead
        if self._iteration <= self._n:
            return self.random_multilabel()

        anchor_class_id, negative_class_id = np.random.choice(
            self._index.keys(), 2)
        anchor_id, positive_id = np.random.choice(
            self._index[anchor_class_id], 2)
        negative_ids = np.random.choice(
            self._index[negative_class_id], self._k)
        # calcualte the smallest simlarity one with negatives
        anchor_label = parse_label(self._labels[anchor_id])
        positive_label = parse_label(self._labels[positive_id])
        negative_labels = [parse_label(self._labels[negative_id]) for
                           negative_id in negative_ids]
        p_sim = intersect_sim(anchor_label, positive_label)
        n_sims = np.array(
            [intersect_sim(anchor_label, negative_label) for
             negative_label in negative_labels])
        min_sim_id = np.argmin(n_sims)
        negative_id = negative_ids[min_sim_id]
        n_sim = n_sims[min_sim_id]
        margin = p_sim - n_sim
        return (anchor_id, positive_id, negative_id, margin) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def find_medioid(self, X, Y):
        """
        Find point with minimal distance to all other points.

        Parameters
        ----------
        X : array
            data set, with N samples x D features.
        Y : array
            labels to select for which samples to compute distances.

        Returns
        -------
        x : array
            medioid
        ix : int
            index of medioid

        """
        # Initiate an array with infinities
        A = np.full((X.shape[0],), np.inf)

        # Insert sum of distances to other points
        A[Y] = np.sum(squareform(pdist(X[Y, :])), axis=1)

        # Find the index of the point with the smallest distance
        ix = np.argmin(A)

        return X[ix, :], ix 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def polar_distance(x1, x2):
    """
    Given two arrays of numbers x1 and x2, pairs the cells that are the
    closest and provides the pairing matrix index: x1(index(1,:)) should be as
    close as possible to x2(index(2,:)). The function outputs the average of 
    the absolute value of the differences abs(x1(index(1,:))-x2(index(2,:))).
    :param x1: vector 1
    :param x2: vector 2
    :return: d: minimum distance between d
             index: the permutation matrix
    """
    x1 = np.reshape(x1, (1, -1), order='F')
    x2 = np.reshape(x2, (1, -1), order='F')
    N1 = x1.size
    N2 = x2.size
    diffmat = np.arccos(np.cos(x1 - np.reshape(x2, (-1, 1), order='F')))
    min_N1_N2 = np.min([N1, N2])
    index = np.zeros((min_N1_N2, 2), dtype=int)
    if min_N1_N2 > 1:
        for k in range(min_N1_N2):
            d2 = np.min(diffmat, axis=0)
            index2 = np.argmin(diffmat, axis=0)
            index1 = np.argmin(d2)
            index2 = index2[index1]
            index[k, :] = [index1, index2]
            diffmat[index2, :] = float('inf')
            diffmat[:, index1] = float('inf')
        d = np.mean(np.arccos(np.cos(x1[:, index[:, 0]] - x2[:, index[:, 1]])))
    else:
        d = np.min(diffmat)
        index = np.argmin(diffmat)
        if N1 == 1:
            index = np.array([1, index])
        else:
            index = np.array([index, 1])
    return d, index 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def _calc_min(self) -> float:
            return self._values[np.argmin(self._values)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [as 別名]
def _calc_around_min(self, num_around: int) -> float:
            min_idx = np.argmin(self._values)
            num_back = min_idx - num_around
            if num_back < 0:
                num_back = 0
            num_forward = min_idx + num_around
            if num_forward > len(self._values) - 1:
                num_forward = len(self._values) - 1
            return np.mean(self._values[num_back: num_forward]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argmin [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)