Python numpy.amax() Examples
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Example #1
Source Project: DOTA_models Author: ringringyi File: np_box_list_ops.py License: Apache License 2.0 | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #2
Source Project: sparse-subspace-clustering-python Author: abhinav4192 File: OutlierDetection.py License: MIT License | 6 votes |
def OutlierDetection(CMat, s): n = np.amax(s) _, N = CMat.shape OutlierIndx = list() FailCnt = 0 Fail = False for i in range(0, N): c = CMat[:, i] if np.sum(np.isnan(c)) >= 1: OutlierIndx.append(i) FailCnt += 1 sc = s.astype(float) sc[OutlierIndx] = np.nan CMatC = CMat.astype(float) CMatC[OutlierIndx, :] = np.nan CMatC[:, OutlierIndx] = np.nan OutlierIndx = OutlierIndx if FailCnt > (N - n): CMatC = np.nan sc = np.nan Fail = True return CMatC, sc, OutlierIndx, Fail
Example #3
Source Project: object_detector_app Author: datitran File: np_box_list_ops.py License: MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #4
Source Project: pyscf Author: pyscf File: gw.py License: Apache License 2.0 | 6 votes |
def get_wmin_wmax_tmax_ia_def(self, tol): from numpy import log, exp, sqrt, where, amin, amax """ This is a default choice of the wmin and wmax parameters for a log grid along imaginary axis. The default choice is based on the eigenvalues. """ E = self.ksn2e[0,0,:] E_fermi = self.fermi_energy E_homo = amax(E[where(E<=E_fermi)]) E_gap = amin(E[where(E>E_fermi)]) - E_homo E_maxdiff = amax(E) - amin(E) d = amin(abs(E_homo-E)[where(abs(E_homo-E)>1e-4)]) wmin_def = sqrt(tol * (d**3) * (E_gap**3)/(d**2+E_gap**2)) wmax_def = (E_maxdiff**2/tol)**(0.250) tmax_def = -log(tol)/ (E_gap) tmin_def = -100*log(1.0-tol)/E_maxdiff return wmin_def, wmax_def, tmin_def,tmax_def
Example #5
Source Project: pyscf Author: pyscf File: gw_iter.py License: Apache License 2.0 | 6 votes |
def si_c_check (self, tol = 1e-5): """ This compares np.solve and LinearOpt-lgmres methods for solving linear equation (1-v\chi_{0}) * W_c = v\chi_{0}v """ import time import numpy as np ww = 1j*self.ww_ia t = time.time() si0_1 = self.si_c(ww) #method 1: numpy.linalg.solve t1 = time.time() - t print('numpy: {} sec'.format(t1)) t2 = time.time() si0_2 = self.si_c2(ww) #method 2: scipy.sparse.linalg.lgmres t3 = time.time() - t2 print('lgmres: {} sec'.format(t3)) summ = abs(si0_1 + si0_2).sum() diff = abs(si0_1 - si0_2).sum() if diff/summ < tol and diff/si0_1.size < tol: print('OK! scipy.lgmres methods and np.linalg.solve have identical results') else: print('Results (W_c) are NOT similar!') return [[diff/summ] , [np.amax(abs(diff))] ,[tol]] #@profile
Example #6
Source Project: pyscf Author: pyscf File: m_ion_log.py License: Apache License 2.0 | 6 votes |
def __init__(self, ao_log, sp): self.ion = ao_log.sp2ion[sp] self.rr,self.pp,self.nr = ao_log.rr,ao_log.pp,ao_log.nr self.interp_rr = log_interp_c(self.rr) self.interp_pp = log_interp_c(self.pp) self.mu2j = ao_log.sp_mu2j[sp] self.nmult= len(self.mu2j) self.mu2s = ao_log.sp_mu2s[sp] self.norbs= self.mu2s[-1] self.mu2ff = ao_log.psi_log[sp] self.mu2ff_rl = ao_log.psi_log_rl[sp] self.mu2rcut = ao_log.sp_mu2rcut[sp] self.rcut = np.amax(self.mu2rcut) self.charge = ao_log.sp2charge[sp]
Example #7
Source Project: pyscf Author: pyscf File: coords2sort_order.py License: Apache License 2.0 | 6 votes |
def coords2sort_order(a2c): """ Delivers a list of atom indices which generates a near-diagonal overlap for a given set of atom coordinates """ na = a2c.shape[0] aa2d = squareform(pdist(a2c)) mxd = np.amax(aa2d)+1.0 a = 0 lsa = [] for ia in range(na): lsa.append(a) asrt = np.argsort(aa2d[a]) for ja in range(1,na): b = asrt[ja] if b not in lsa: break aa2d[a,b] = aa2d[b,a] = mxd a = b return np.array(lsa)
Example #8
Source Project: pyscf Author: pyscf File: test_0092_ag2_ae.py License: Apache License 2.0 | 6 votes |
def test_gw(self): """ This is GW """ mol = gto.M(verbose=0, atom='''Ag 0 0 -0.3707; Ag 0 0 0.3707''', basis = 'cc-pvdz-pp',) gto_mf = scf.RHF(mol)#.density_fit() gto_mf.kernel() #print('gto_mf.mo_energy:', gto_mf.mo_energy) s = nao(mf=gto_mf, gto=mol, verbosity=0) oref = s.overlap_coo().toarray() #print('s.norbs:', s.norbs, oref.sum()) pb = prod_basis(nao=s, algorithm='fp') pab2v = pb.get_ac_vertex_array() mom0,mom1=pb.comp_moments() orec = np.einsum('p,pab->ab', mom0, pab2v) self.assertTrue(np.allclose(orec,oref, atol=1e-3), \ "{} {}".format(abs(orec-oref).sum()/oref.size, np.amax(abs(orec-oref))))
Example #9
Source Project: NanoPlot Author: wdecoster File: spatial_heatmap.py License: GNU General Public License v3.0 | 6 votes |
def spatial_heatmap(array, path, title=None, color="Greens", figformat="png"): """Taking channel information and creating post run channel activity plots.""" logging.info("Nanoplotter: Creating heatmap of reads per channel using {} reads." .format(array.size)) activity_map = Plot( path=path + "." + figformat, title="Number of reads generated per channel") layout = make_layout(maxval=np.amax(array)) valueCounts = pd.value_counts(pd.Series(array)) for entry in valueCounts.keys(): layout.template[np.where(layout.structure == entry)] = valueCounts[entry] plt.figure() ax = sns.heatmap( data=pd.DataFrame(layout.template, index=layout.yticks, columns=layout.xticks), xticklabels="auto", yticklabels="auto", square=True, cbar_kws={"orientation": "horizontal"}, cmap=color, linewidths=0.20) ax.set_title(title or activity_map.title) activity_map.fig = ax.get_figure() activity_map.save(format=figformat) plt.close("all") return [activity_map]
Example #10
Source Project: pyqmc Author: WagnerGroup File: reblock.py License: MIT License | 6 votes |
def optimally_reblocked(data): """ Find optimal reblocking of input data. Takes in pandas DataFrame of raw data to reblock, returns DataFrame of reblocked data. """ opt = opt_block(data) n_reblock = int(np.amax(opt)) rb_data = reblock_by2(data, n_reblock) serr = rb_data.sem(axis=0) d = { "mean": rb_data.mean(axis=0), "standard error": serr, "standard error error": serr / np.sqrt(2 * (len(rb_data) - 1)), "reblocks": n_reblock, } return pd.DataFrame(d)
Example #11
Source Project: vehicle_counting_tensorflow Author: ahmetozlu File: np_box_list_ops.py License: MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #12
Source Project: vehicle_counting_tensorflow Author: ahmetozlu File: np_box_mask_list_ops.py License: MIT License | 6 votes |
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0): """Prunes the boxes in list1 that overlap less than thresh with list2. For each mask in box_mask_list1, we want its IOA to be more than minoverlap with at least one of the masks in box_mask_list2. If it does not, we remove it. If the masks are not full size image, we do the pruning based on boxes. Args: box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks. box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned box_mask_list with size [N', 4]. """ intersection_over_area = ioa(box_mask_list2, box_mask_list1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_box_mask_list1 = gather(box_mask_list1, keep_inds) return new_box_mask_list1
Example #13
Source Project: LearningX Author: ankonzoid File: cartpole.py License: MIT License | 6 votes |
def train(self, batch_size=32): # Train using replay experience minibatch = random.sample(self.memory, batch_size) for memory in minibatch: state, action, reward, state_next, done = memory # Build Q target: # -> Qtarget[!action] = Q[!action] # Qtarget[action] = reward + gamma * max[a'](Q_next(state_next)) Qtarget = self.model.predict(state) dQ = reward if not done: dQ += self.gamma * np.amax(self.model.predict(state_next)[0]) Qtarget[0][action] = dQ self.model.fit(state, Qtarget, epochs=1, verbose=0) # Decary exploration after training if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay
Example #14
Source Project: pytim Author: Marcello-Sega File: utilities.py License: GNU General Public License v3.0 | 6 votes |
def guess_normal(universe, group): """ Guess the normal of a liquid slab """ universe.atoms.pack_into_box() dim = universe.coord.dimensions delta = [] for direction in range(0, 3): histo, _ = np.histogram( group.positions[:, direction], bins=5, range=(0, dim[direction]), density=True) max_val = np.amax(histo) min_val = np.amin(histo) delta.append(np.sqrt((max_val - min_val)**2)) if np.max(delta) / np.min(delta) < 5.0: print("Warning: the result of the automatic normal detection (", np.argmax(delta), ") is not reliable") return np.argmax(delta)
Example #15
Source Project: aboleth Author: gradientinstitute File: multi_input.py License: Apache License 2.0 | 6 votes |
def input_fn(df): """Format the downloaded data.""" # Creates a dictionary mapping from each continuous feature column name (k) # to the values of that column stored in a constant Tensor. continuous_cols = [df[k].values for k in CONTINUOUS_COLUMNS] X_con = np.stack(continuous_cols).astype(np.float32).T # Standardise X_con -= X_con.mean(axis=0) X_con /= X_con.std(axis=0) # Creates a dictionary mapping from each categorical feature column name categ_cols = [np.where(pd.get_dummies(df[k]).values)[1][:, np.newaxis] for k in CATEGORICAL_COLUMNS] n_values = [np.amax(c) + 1 for c in categ_cols] X_cat = np.concatenate(categ_cols, axis=1).astype(np.int32) # Converts the label column into a constant Tensor. label = df[LABEL_COLUMN].values[:, np.newaxis] # Returns the feature columns and the label. return X_con, X_cat, n_values, label
Example #16
Source Project: SlowFast-Network-pytorch Author: MagicChuyi File: np_box_list_ops.py License: MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #17
Source Project: SlowFast-Network-pytorch Author: MagicChuyi File: np_box_mask_list_ops.py License: MIT License | 6 votes |
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0): """Prunes the boxes in list1 that overlap less than thresh with list2. For each mask in box_mask_list1, we want its IOA to be more than minoverlap with at least one of the masks in box_mask_list2. If it does not, we remove it. If the masks are not full size image, we do the pruning based on boxes. Args: box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks. box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned box_mask_list with size [N', 4]. """ intersection_over_area = ioa(box_mask_list2, box_mask_list1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_box_mask_list1 = gather(box_mask_list1, keep_inds) return new_box_mask_list1
Example #18
Source Project: DeepFloorplan Author: zlzeng File: util.py License: GNU General Public License v3.0 | 6 votes |
def refine_room_region(cw_mask, rm_ind): label_rm, num_label = ndimage.label((1-cw_mask)) new_rm_ind = np.zeros(rm_ind.shape) for j in xrange(1, num_label+1): mask = (label_rm == j).astype(np.uint8) ys, xs = np.where(mask!=0) area = (np.amax(xs)-np.amin(xs))*(np.amax(ys)-np.amin(ys)) if area < 100: continue else: room_types, type_counts = np.unique(mask*rm_ind, return_counts=True) if len(room_types) > 1: room_types = room_types[1:] # ignore background type which is zero type_counts = type_counts[1:] # ignore background count new_rm_ind += mask*room_types[np.argmax(type_counts)] return new_rm_ind
Example #19
Source Project: tf-pose Author: SrikanthVelpuri File: pose_dataset.py License: Apache License 2.0 | 6 votes |
def get_heatmap(self, target_size): heatmap = np.zeros((CocoMetadata.__coco_parts, self.height, self.width), dtype=np.float32) for joints in self.joint_list: for idx, point in enumerate(joints): if point[0] < 0 or point[1] < 0: continue CocoMetadata.put_heatmap(heatmap, idx, point, self.sigma) heatmap = heatmap.transpose((1, 2, 0)) # background heatmap[:, :, -1] = np.clip(1 - np.amax(heatmap, axis=2), 0.0, 1.0) if target_size: heatmap = cv2.resize(heatmap, target_size, interpolation=cv2.INTER_AREA) return heatmap.astype(np.float16)
Example #20
Source Project: DOTA_models Author: ringringyi File: np_box_list_ops.py License: Apache License 2.0 | 5 votes |
def gather(boxlist, indices, fields=None): """Gather boxes from BoxList according to indices and return new BoxList. By default, Gather returns boxes corresponding to the input index list, as well as all additional fields stored in the boxlist (indexing into the first dimension). However one can optionally only gather from a subset of fields. Args: boxlist: BoxList holding N boxes indices: a 1-d numpy array of type int_ fields: (optional) list of fields to also gather from. If None (default), all fields are gathered from. Pass an empty fields list to only gather the box coordinates. Returns: subboxlist: a BoxList corresponding to the subset of the input BoxList specified by indices Raises: ValueError: if specified field is not contained in boxlist or if the indices are not of type int_ """ if indices.size: if np.amax(indices) >= boxlist.num_boxes() or np.amin(indices) < 0: raise ValueError('indices are out of valid range.') subboxlist = np_box_list.BoxList(boxlist.get()[indices, :]) if fields is None: fields = boxlist.get_extra_fields() for field in fields: extra_field_data = boxlist.get_field(field) subboxlist.add_field(field, extra_field_data[indices, ...]) return subboxlist
Example #21
Source Project: kitti-object-eval-python Author: traveller59 File: kitti_common.py License: MIT License | 5 votes |
def filter_kitti_anno(image_anno, used_classes, used_difficulty=None, dontcare_iou=None): if not isinstance(used_classes, (list, tuple)): used_classes = [used_classes] img_filtered_annotations = {} relevant_annotation_indices = [ i for i, x in enumerate(image_anno['name']) if x in used_classes ] for key in image_anno.keys(): img_filtered_annotations[key] = ( image_anno[key][relevant_annotation_indices]) if used_difficulty is not None: relevant_annotation_indices = [ i for i, x in enumerate(img_filtered_annotations['difficulty']) if x in used_difficulty ] for key in image_anno.keys(): img_filtered_annotations[key] = ( img_filtered_annotations[key][relevant_annotation_indices]) if 'DontCare' in used_classes and dontcare_iou is not None: dont_care_indices = [ i for i, x in enumerate(img_filtered_annotations['name']) if x == 'DontCare' ] # bounding box format [y_min, x_min, y_max, x_max] all_boxes = img_filtered_annotations['bbox'] ious = iou(all_boxes, all_boxes[dont_care_indices]) # Remove all bounding boxes that overlap with a dontcare region. if ious.size > 0: boxes_to_remove = np.amax(ious, axis=1) > dontcare_iou for key in image_anno.keys(): img_filtered_annotations[key] = (img_filtered_annotations[key][ np.logical_not(boxes_to_remove)]) return img_filtered_annotations
Example #22
Source Project: object_detector_app Author: datitran File: np_box_list_ops.py License: MIT License | 5 votes |
def gather(boxlist, indices, fields=None): """Gather boxes from BoxList according to indices and return new BoxList. By default, Gather returns boxes corresponding to the input index list, as well as all additional fields stored in the boxlist (indexing into the first dimension). However one can optionally only gather from a subset of fields. Args: boxlist: BoxList holding N boxes indices: a 1-d numpy array of type int_ fields: (optional) list of fields to also gather from. If None (default), all fields are gathered from. Pass an empty fields list to only gather the box coordinates. Returns: subboxlist: a BoxList corresponding to the subset of the input BoxList specified by indices Raises: ValueError: if specified field is not contained in boxlist or if the indices are not of type int_ """ if indices.size: if np.amax(indices) >= boxlist.num_boxes() or np.amin(indices) < 0: raise ValueError('indices are out of valid range.') subboxlist = np_box_list.BoxList(boxlist.get()[indices, :]) if fields is None: fields = boxlist.get_extra_fields() for field in fields: extra_field_data = boxlist.get_field(field) subboxlist.add_field(field, extra_field_data[indices, ...]) return subboxlist
Example #23
Source Project: pymoo Author: msu-coinlab File: rmetric.py License: Apache License 2.0 | 5 votes |
def _preprocess(self, data, ref_point, w_point): datasize = np.size(data, 0) # Identify representative point ref_matrix = np.tile(ref_point, (datasize, 1)) w_matrix = np.tile(w_point, (datasize, 1)) # ratio of distance to the ref point over the distance between the w_point and the ref_point diff_matrix = (data - ref_matrix) / (w_matrix - ref_matrix) agg_value = np.amax(diff_matrix, axis=1) idx = np.argmin(agg_value) zp = [data[idx, :]] return zp,
Example #24
Source Project: pyscf Author: pyscf File: m_prod_basis_obsolete.py License: Apache License 2.0 | 5 votes |
def overlap_check(self, **kw): """ Our standard minimal check comparing with overlaps """ sref = self.sv.overlap_coo(**kw).toarray() mom0,mom1 = self.comp_moments() vpab = self.get_ac_vertex_array() sprd = np.einsum('p,pab->ab', mom0,vpab) return [[abs(sref-sprd).sum()/sref.size, np.amax(abs(sref-sprd))]]
Example #25
Source Project: pyscf Author: pyscf File: test_0095_h2_ae_rescf.py License: Apache License 2.0 | 5 votes |
def test_gw(self): """ This is GW """ mol = gto.M( verbose = 0, atom = '''H 0.0 0.0 -0.3707; H 0.0 0.0 0.3707''', basis = 'def2-TZVP',) gto_mf = scf.RHF(mol)#.density_fit() gto_mf.kernel() print('gto_mf.mo_energy:', gto_mf.mo_energy) s = nao(mf=gto_mf, gto=mol, verbosity=0) oref = s.overlap_coo().toarray() print('s.norbs:', s.norbs, oref.sum()) pb = prod_basis(nao=s, algorithm='fp') pab2v = pb.get_ac_vertex_array() mom0,mom1=pb.comp_moments() orec = np.einsum('p,pab->ab', mom0, pab2v) print( abs(orec-oref).sum()/oref.size, np.amax(abs(orec-oref)) )
Example #26
Source Project: pyscf Author: pyscf File: prod_basis.py License: Apache License 2.0 | 5 votes |
def overlap_check(self, **kw): """ Our standard minimal check comparing with overlaps """ sref = self.sv.overlap_coo(**kw).toarray() mom0,mom1 = self.comp_moments() vpab = self.get_ac_vertex_array() sprd = np.einsum('p,pab->ab', mom0,vpab) return [[abs(sref-sprd).sum()/sref.size, np.amax(abs(sref-sprd))]]
Example #27
Source Project: NanoPlot Author: wdecoster File: timeplots.py License: GNU General Public License v3.0 | 5 votes |
def length_over_time(dfs, path, figformat, title, log_length=False, plot_settings={}): if log_length: time_length = Plot(path=path + "TimeLogLengthViolinPlot." + figformat, title="Violin plot of log read lengths over time") else: time_length = Plot(path=path + "TimeLengthViolinPlot." + figformat, title="Violin plot of read lengths over time") sns.set(style="white", **plot_settings) if log_length: length_column = "log_lengths" else: length_column = "lengths" if "length_filter" in dfs: # produced by NanoPlot filtering of too long reads temp_dfs = dfs[dfs["length_filter"]] else: temp_dfs = dfs ax = sns.violinplot(x="timebin", y=length_column, data=temp_dfs, inner=None, cut=0, linewidth=0) ax.set(xlabel='Interval (hours)', ylabel="Read length", title=title or time_length.title) if log_length: ticks = [10**i for i in range(10) if not 10**i > 10 * np.amax(dfs["lengths"])] ax.set(yticks=np.log10(ticks), yticklabels=ticks) plt.xticks(rotation=45, ha='center', fontsize=8) time_length.fig = ax.get_figure() time_length.save(format=figformat) plt.close("all") return time_length
Example #28
Source Project: Mastering-Machine-Learning-Algorithms Author: PacktPublishing File: actor_critic_td0.py License: MIT License | 5 votes |
def get_softmax_policy(): softmax_policy = policy_importances - np.amax(policy_importances, axis=2, keepdims=True) return np.exp(softmax_policy) / np.sum(np.exp(softmax_policy), axis=2, keepdims=True)
Example #29
Source Project: View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition Author: microsoft File: data_rnn.py License: MIT License | 5 votes |
def softmax(data): e = np.exp(data - np.amax(data, axis=-1, keepdims=True)) s = np.sum(e, axis=-1, keepdims=True) return e / s
Example #30
Source Project: pyqmc Author: WagnerGroup File: supercell.py License: MIT License | 5 votes |
def get_supercell_kpts(supercell): Sinv = np.linalg.inv(supercell.S).T u = [0, 1] unit_box = np.stack([x.ravel() for x in np.meshgrid(*[u] * 3, indexing="ij")]).T unit_box_ = np.dot(unit_box, supercell.S.T) xyz_range = np.stack([f(unit_box_, axis=0) for f in (np.amin, np.amax)]).T kptmesh = np.meshgrid(*[np.arange(*r) for r in xyz_range], indexing="ij") possible_kpts = np.dot(np.stack([x.ravel() for x in kptmesh]).T, Sinv) in_unit_box = (possible_kpts >= 0) * (possible_kpts < 1 - 1e-12) select = np.where(np.all(in_unit_box, axis=1))[0] reclatvec = np.linalg.inv(supercell.original_cell.lattice_vectors()).T * 2 * np.pi return np.dot(possible_kpts[select], reclatvec)