Python numpy.nanargmin方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def mouse_drag(self, event):
        '''

        '''

        if event.inaxes == self.ax and event.button == 1:

            # Index of nearest point
            i = np.nanargmin(((event.xdata - self.x) / self.nx) ** 2)
            j = np.nanargmin(((event.ydata - self.y) / self.ny) ** 2)

            if (i == self.last_i) and (j == self.last_j):
                return
            else:
                self.last_i = i
                self.last_j = j

            # Toggle pixel
            if self.aperture[j, i]:
                self.aperture[j, i] = 0
            else:
                self.aperture[j, i] = 1

            # Update the contour
            self.update() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def mouse_click(self, event):
        '''

        '''

        if event.mouseevent.inaxes == self.ax:

            # Index of nearest point
            i = np.nanargmin(
                ((event.mouseevent.xdata - self.x) / self.nx) ** 2)
            j = np.nanargmin(
                ((event.mouseevent.ydata - self.y) / self.ny) ** 2)
            self.last_i = i
            self.last_j = j

            # Toggle pixel
            if self.aperture[j, i]:
                self.aperture[j, i] = 0
            else:
                self.aperture[j, i] = 1

            # Update the contour
            self.update() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def _compute_eps(orders, rdp, delta):
  """Compute epsilon given a list of RDP values and target delta.

  Args:
    orders: An array (or a scalar) of orders.
    rdp: A list (or a scalar) of RDP guarantees.
    delta: The target delta.

  Returns:
    Pair of (eps, optimal_order).

  Raises:
    ValueError: If input is malformed.

  """
  orders_vec = np.atleast_1d(orders)
  rdp_vec = np.atleast_1d(rdp)

  if len(orders_vec) != len(rdp_vec):
    raise ValueError("Input lists must have the same length.")

  eps = rdp_vec - math.log(delta) / (orders_vec - 1)

  idx_opt = np.nanargmin(eps)  # Ignore NaNs
  return eps[idx_opt], orders_vec[idx_opt] 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def OnKeyPressed(self, event=None):
        if event.GetKeyCode() == wx.WXK_RIGHT:
            self.nextImage(event=None)
        elif event.GetKeyCode() == wx.WXK_LEFT:
            self.prevImage(event=None)
        elif event.GetKeyCode() == wx.WXK_BACK:
            pos_abs = event.GetPosition()
            inv = self.axes.transData.inverted()
            pos_rel = list(inv.transform(pos_abs))
            pos_rel[1] = (
                self.axes.get_ylim()[0] - pos_rel[1]
            )  # Recall y-axis is inverted
            i = np.nanargmin(
                [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
            )
            closest_dp = self.drs[i]
            msg = wx.MessageBox(
                "Do you want to remove the label %s ?" % closest_dp.bodyParts,
                "Remove!",
                wx.YES_NO | wx.ICON_WARNING,
            )
            if msg == 2:
                closest_dp.delete_data() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def OnKeyPressed(self, event=None):
        if event.GetKeyCode() == wx.WXK_RIGHT:
            self.nextImage(event=None)
        elif event.GetKeyCode() == wx.WXK_LEFT:
            self.prevImage(event=None)
        elif event.GetKeyCode() == wx.WXK_BACK:
            pos_abs = event.GetPosition()
            inv = self.axes.transData.inverted()
            pos_rel = list(inv.transform(pos_abs))
            pos_rel[1] = (
                self.axes.get_ylim()[0] - pos_rel[1]
            )  # Recall y-axis is inverted
            i = np.nanargmin(
                [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
            )
            closest_dp = self.drs[i]
            msg = wx.MessageBox(
                f"Do you want to remove the label {closest_dp.individual_name}:{closest_dp.bodyParts}?",
                "Remove!",
                wx.YES_NO | wx.ICON_WARNING,
            )
            if msg == 2:
                closest_dp.delete_data() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def save_everything(args, metrics_hist_all, model, model_dir, params, criterion, evaluate=False):
    """
        Save metrics, model, params all in model_dir
    """
    save_metrics(metrics_hist_all, model_dir)
    params['model_dir'] = model_dir
    save_params_dict(params)

    if not evaluate:
        #save the model with the best criterion metric
        if not np.all(np.isnan(metrics_hist_all[0][criterion])):
            if criterion == 'loss_dev': 
                eval_val = np.nanargmin(metrics_hist_all[0][criterion])
            else:
                eval_val = np.nanargmax(metrics_hist_all[0][criterion])

            if eval_val == len(metrics_hist_all[0][criterion]) - 1:                

		#save state dict
                sd = model.cpu().state_dict()
                torch.save(sd, model_dir + "/model_best_%s.pth" % criterion)
                if args.gpu:
                    model.cuda()
    print("saved metrics, params, model to directory %s\n" % (model_dir)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def weighted_minhash(v, sample_size, rs, ln_cs, betas):
    if sample_size != rs.shape[0]:
        raise ValueError("Input sample size mismatch, expecting %d" % rs.shape[0])
    if len(v) != rs.shape[1]:
        raise ValueError("Input dimension mismatch, expecting %d" % rs.shape[1])

    hashvalues = numpy.zeros((sample_size, 2), dtype=numpy.uint32)
    vzeros = (v == 0)
    if vzeros.all():
        raise ValueError("Input is all zeros")
    v[vzeros] = numpy.nan
    vlog = numpy.log(v)
    v[vzeros] = 0
    for i in range(sample_size):
        t = numpy.floor((vlog / rs[i]) + betas[i])
        ln_y = (t - betas[i]) * rs[i]
        ln_a = ln_cs[i] - ln_y - rs[i]
        k = numpy.nanargmin(ln_a)
        hashvalues[i][0], hashvalues[i][1] = k, int(t[k])
    return hashvalues 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def early_stop_decision(self, epoch, val_metric, val_loss):
        '''
	Stop training if validation loss has stopped decreasing and
	validation BLEU score has not increased for --patience epochs.

        WARNING: quits with sys.exit(0).

	TODO: this doesn't yet support early stopping based on TER
        '''

	if val_loss < self.best_val_loss:
	    self.wait = 0
        elif val_metric > self.best_val_metric or self.args.no_early_stopping:
            self.wait = 0
        else:
            self.wait += 1
            if self.wait >= self.patience:
                # we have exceeded patience
                if val_loss > self.best_val_loss:
                    # and loss is no longer decreasing
                    logger.info("Epoch %d: early stopping", epoch)
                    handle = open("checkpoints/%s/summary"
                                  % self.args.run_string, "a")
                    handle.write("Early stopping because patience exceeded\n")
                    best_bleu = np.nanargmax(self.val_metric)
                    best_loss = np.nanargmin(self.val_loss)
                    logger.info("Best Metric: %d | val loss %.5f score %.2f",
                                best_bleu+1, self.val_loss[best_bleu],
                                self.val_metric[best_bleu])
                    logger.info("Best loss: %d | val loss %.5f score %.2f",
                                best_loss+1, self.val_loss[best_loss],
                                self.val_metric[best_loss])
                    handle.close()
                    sys.exit(0) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def log_performance(self):
        '''
        Record model performance so far, based on validation loss.
        '''
        handle = open("checkpoints/%s/summary" % self.args.run_string, "w")

        for epoch in range(len(self.val_loss)):
            handle.write("Checkpoint %d | val loss: %.5f bleu %.2f\n"
                         % (epoch+1, self.val_loss[epoch],
                            self.val_metric[epoch]))

        logger.info("---")  # break up the presentation for clarity

        # BLEU is the quickest indicator of performance for our task
        # but loss is our objective function
        best_bleu = np.nanargmax(self.val_metric)
        best_loss = np.nanargmin(self.val_loss)
        logger.info("Best Metric: %d | val loss %.5f score %.2f",
                    best_bleu+1, self.val_loss[best_bleu],
                    self.val_metric[best_bleu])
        handle.write("Best Metric: %d | val loss %.5f score %.2f\n"
                     % (best_bleu+1, self.val_loss[best_bleu],
                        self.val_metric[best_bleu]))
        logger.info("Best loss: %d | val loss %.5f score %.2f",
                    best_loss+1, self.val_loss[best_loss],
                    self.val_metric[best_loss])
        handle.write("Best loss: %d | val loss %.5f score %.2f\n"
                     % (best_loss+1, self.val_loss[best_loss],
                        self.val_metric[best_loss]))
        logger.info("Early stopping marker: wait/patience: %d/%d\n",
                    self.wait, self.patience)
        handle.write("Early stopping marker: wait/patience: %d/%d\n" %
                     (self.wait, self.patience))
        handle.close() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def test_nanfunctions_matrices_general():
    # Check that it works and that type and
    # shape are preserved
    # 2018-04-29: moved here from core.tests.test_nanfunctions
    mat = np.matrix(np.eye(3))
    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
              np.nanmean, np.nanvar, np.nanstd):
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 1))
        res = f(mat)
        assert_(np.isscalar(res))

    for f in np.nancumsum, np.nancumprod:
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3*3)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def test_nanargmin(self):
        tgt = np.argmin(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanargmin(mat), tgt) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def _(artist, event):
    # No need to call `line.contains` as we're going to redo the work anyways
    # (also see matplotlib/matplotlib#6645, though that's fixed in mpl2.1).

    # Always work in screen coordinates, as this is how we need to compute
    # distances.  Note that the artist transform may be different from the axes
    # transform (e.g., for axvline).
    xy = event.x, event.y
    data_xy = artist.get_xydata()
    data_screen_xy = artist.get_transform().transform(data_xy)
    sels = []
    # If markers are visible, find the closest vertex.
    if artist.get_marker() not in ["None", "none", " ", "", None]:
        ds = np.hypot(*(xy - data_screen_xy).T)
        try:
            argmin = np.nanargmin(ds)
        except ValueError:  # Raised by nanargmin([nan]).
            pass
        else:
            target = _with_attrs(
                _untransform(  # More precise than transforming back.
                    data_xy[argmin], data_screen_xy[argmin], artist.axes),
                index=argmin)
            sels.append(Selection(artist, target, ds[argmin], None, None))
    # If lines are visible, find the closest projection.
    if (artist.get_linestyle() not in ["None", "none", " ", "", None]
            and len(artist.get_xydata()) > 1):
        sel = _compute_projection_pick(artist, artist.get_path(), xy)
        if sel is not None:
            sel.target.index = {
                "_draw_lines": lambda _, index: index,
                "_draw_steps_pre": Index.pre_index,
                "_draw_steps_mid": Index.mid_index,
                "_draw_steps_post": Index.post_index}[
                    Line2D.drawStyles[artist.get_drawstyle()]](
                        len(data_xy), sel.target.index)
            sels.append(sel)
    sel = min(sels, key=lambda sel: sel.dist, default=None)
    return sel if sel and sel.dist < artist.get_pickradius() else None 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def train(self, alpha):
        """
        Finds the agglomerative clustering on the data alpha
        :param alpha: angles in radians
        :returns: data, cluster ids

        """
        assert len(alpha.shape) == 1, 'Clustering works only for 1d data'
        n = len(alpha)
        cid = np.arange(n, dtype=int)

        nu = n


        while nu > self.numclust:
            mu = np.asarray([descr.mean(alpha[cid == j]) if j in cid else np.Inf for j in range(n)])
            D = np.abs(descr.pairwise_cdiff(mu))
            idx = np.triu_indices(n,1)
            min = np.nanargmin(D[idx])
            cid[cid == cid[idx[0][min]]] = cid[idx[1][min]]
            nu -= 1


        cid2 = np.empty_like(cid)
        for i,j in enumerate(np.unique(cid)):
            cid2[cid == j] = i
        ucid = np.unique(cid2)
        self.centroids = np.asarray([descr.mean(alpha[cid2 == i]) for i in ucid])
        self.cluster_ids = ucid
        self.r = np.asarray([descr.resultant_vector_length(alpha[cid2 == i]) for i in ucid])

        return alpha, cid2 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def cal_eer(fpr, tpr):
    # makes fpr + tpr = 1
    eer = fpr[np.nanargmin(np.absolute((fpr + tpr - 1)))]
    return eer 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import nanargmin [as 别名]
def _get_best_estimate_single_method(derivative, errors):
    """Select best derivative estimates element wise.

    Given a single method, e.g. central differences with 2 num_terms (see above), we get
    multiple Richardson approximations including estimated errors. Here we select the
    approximations which result in the lowest error element wise.

    Args:
        derivative (np.ndarray): Derivative estimates from Richardson approximation.
            First axis (axis 0) denotes the potentially multiple estimates. Following
            dimensions represent the dimension of the derivative, i.e. for a classical
            gradient ``derivative`` has 2 dimensions, while for a classical jacobian
            ``derivative`` has 3 dimensions.
        errors (np.ndarray): Error estimates of ``derivative`` estimates. Has the same
            shape as ``derivative``.

    Returns:
        derivative_minimal (np.ndarray): Best derivate estimates chosen with respect
            to minimizing ``errors``. Note that the best values are selected
            element-wise. Has shape ``(derivative.shape[1], derivative.shape[2])``.

        error_minimal (np.ndarray): Minimal errors selected element-wise along axis
            0 of ``errors``.

    """
    if derivative.shape[0] == 1:
        derivative_minimal = np.squeeze(derivative, axis=0)
        error_minimal = np.squeeze(errors, axis=0)
    else:

        minimizer = np.nanargmin(errors, axis=0)

        derivative_minimal = np.take_along_axis(
            derivative, minimizer[np.newaxis, :], axis=0
        )
        derivative_minimal = np.squeeze(derivative_minimal, axis=0)
        error_minimal = np.nanmin(errors, axis=0)

    return derivative_minimal, error_minimal