Norfolk Senior Cup stats & predictions

John Smith (Team A)

Known for his exceptional goal-scoring ability, John Smith has been in top form this season. His knack for finding the back of the net makes him a critical asset for Team A.

• Key Statistic: Scored in each of his last five appearances.
• Betting Angle: Back him for any goalscorer.

Mary Johnson (Team B)

As one of the league's top defenders, Mary Johnson's leadership at the back is instrumental in keeping her team in games. Her ability to read the game makes her a formidable presence on the field.

• Key Statistic: Conceded only one goal in her last six matches.
• Betting Angle: Consider an under bet on goals for this match.

Alex Brown (Team C)

Alex Brown's creativity and vision make him a key playmaker for Team C. His assists have been crucial in setting up goals and maintaining momentum during matches.

• Key Statistic: Provided assists in four consecutive games.
• Betting Angle: Back him for an assist.

Tactics for Team A

With a focus on high pressing and quick transitions, Team A aims to exploit any lapses in their opponents' defense. Their strategy revolves around creating scoring opportunities through aggressive play.

• Holding Midfielder Role: Control the tempo and distribute the ball effectively.
• Flexibility in Formation: Adapt quickly between formations based on game flow.
• Foul Play Strategy: Use tactical fouls to disrupt opponents' rhythm.

Tactics for Team B

This team prioritizes defensive solidity while looking to counter-attack swiftly when opportunities arise. Their game plan focuses on absorbing pressure and capitalizing on set-pieces.

• Solid Defensive Line: Maintain structure and discipline at the back.
< [0]: # Copyright (c) OpenMMLab. All rights reserved. [1]: import numpy as np [2]: from numpy.testing import assert_array_equal [3]: import pytest [4]: from mmpose.core.post_processing import assign_and_sample [5]: from mmpose.datasets.builder import build_posetrack17_test_dataset [6]: def _get_gt_kps(num_people=10): [7]: gt_kps = np.random.rand(num_people, np.random.randint(1, high=10), [8]: np.random.randint(1, high=4)) [9]: return gt_kps [10]: def _get_proposals(num_proposals=10): [11]: proposals = np.random.rand(num_proposals, np.random.randint(1, high=10), [12]: np.random.randint(1, high=4)) [13]: return proposals [14]: def _get_assign_result(gt_kps=_get_gt_kps(), proposals=_get_proposals()): [15]: num_gt = len(gt_kps) [16]: num_proposals = len(proposals) [17]: assigned_gt_inds = np.concatenate([ [18]: np.full( [19]: num_proposals // num_gt, [20]: i, [21]: dtype=np.int32) if i != num_gt - 1 else np.full( [22]: num_proposals // num_gt + num_proposals % num_gt, [23]: i, [24]: dtype=np.int32) for i in range(num_gt) [25]: ]) [26]: assigned_labels = np.concatenate([ [27]: np.full(num_proposals // num_gt + num_proposals % num_gt, [28]: i, [29]: dtype=np.int32) for i in range(num_gt) [30]: ]) [31]: pos_weight = ( [32]: np.concatenate([ [33]: np.full(num_proposals // num_gt + int(i == num_gt - 1), [34]: 1.0 - float(i + 1) / float(num_gt + 1)) for i in range(num_gt) [35]: ]) if num_proposals > num_gt else np.ones_like(assigned_gt_inds)) [36]: return { [37]: 'assigned_gt_inds': assigned_gt_inds, [38]: 'assigned_labels': assigned_labels, [39]: 'pos_weight': pos_weight [40]: } [41]: def test_assign_and_sample(): [42]: dataset_cfg = dict( [43]: type='TopDownCocoDataset', [44]: data_cfg=dict( [45]: type='TopDownCocoDatasetConfig', [46]: img_prefix='/path/to/TopDownCocoDataset', [47]: data_root='/path/to/TopDownCocoDataset')) [48]: test_dataset_cfg = dict( [49]: type='TopDownPosetrack17Dataset', [50]: ann_file='/path/to/TopDownPosetrack17Dataset/annotations/test.json', [51]: img_prefix='/path/to/TopDownPosetrack17Dataset', [52]: data_cfg=dataset_cfg) [53]: test_dataset = build_posetrack17_test_dataset(test_dataset_cfg) ***** Tag Data ***** ID: 2 description: The function `_get_assign_result` constructs assignment results including ground truth indices and labels based on randomly generated keypoints and proposals. start line: 14 end line: 40 dependencies: - type: Function name: _get_gt_kps start line: 6 end line: 9 - type: Function name: _get_proposals start line: 10 end line: 13 context description: This function demonstrates how ground truth keypoints and proposals can be used to create assignments which are essential for object detection tasks. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students:5 self contained: Y ************* ## Suggestions for complexity 1. **Dynamic Proposal Weighting:** Modify `_get_assign_result` so that it dynamically adjusts `pos_weight` based on proposal confidence scores or other criteria rather than just evenly distributing weights across ground truth instances. 2. **Multi-Class Assignments:** Extend `_get_assign_result` to handle multi-class assignments where each proposal can belong to multiple classes with different probabilities. 3. **Time-Series Data Handling:** Adapt `_get_assign_result` to work with time-series data where both `gt_kps` and `proposals` have an additional temporal dimension. 4. **Batch Processing:** Enable `_get_assign_result` to process batches of ground truth keypoints and proposals simultaneously rather than single instances. 5. **Custom Sampling Strategies:** Introduce custom sampling strategies within `_get_assign_result` that prioritize certain ground truth instances over others based on specific criteria such as distance metrics or feature similarities. ## Conversation <|user|>_Need help with this code [SNIPPET]. Want dynamic pos_weight based on proposal confidence.<|end|> <|assistant|>_Sure! You can modify `_get_assign_result` to accept an additional parameter `proposal_confidences`, which contains confidence scores for each proposal. Then use these scores to adjust `pos_weight`. Here’s how you can do it: python def _get_assign_result(gt_kps=_get_gt_kps(), proposals=_get_proposals(), proposal_confidences=None): num_gt = len(gt_kps) num_proposals = len(proposals) if proposal_confidences is None: proposal_confidences = np.ones(num_proposals) assigned_gt_inds = np.concatenate([ np.full( num_proposals // num_gt, i, dtype=np.int32) if i != num_gt -1 else np.full( num_proposals // num_gt + num_proposals % num_gt, i, dtype=np.int32) for i in range(num_gt) ]) assigned_labels = np.concatenate([ np.full(num_proposals // num_gt + num_proposals % num_gt, i, dtype=np.int32) for i in range(num_gt) ]) pos_weight = ( np.concatenate([ proposal_confidences[num_proposals // num_gt * i : (num_proposals // num_gt * (i+1)) + (i == num_gt -1) * (num_proposals % num_gt)] * (1.0 - float(i +1) / float(num_gt +1)) for i in range(num_gt) ]) if num_proposals > num_gt else proposal_confidences * np.ones_like(assigned_gt_inds)) return { 'assigned_gt_inds': assigned_gt_inds, 'assigned_labels': assigned_labels, 'pos_weight': pos_weight } This way, `pos_weight` will be influenced by `proposal_confidences`.<|end|> <|user|>_Good but need more control over weights using distance metrics instead.<|end|> <|assistant|>_Got it! Let’s add another parameter `distance_metrics` which contains distances between each proposal and its nearest ground truth keypoint. We'll use these distances to adjust `pos_weight`. Here’s how you can modify it: python def _get_assign_result(gt_kps=_get_gt_kps(), proposals=_get_proposals(), distance_metrics=None): import numpy as np num_gt = len(gt_kps) num_proposals = len(proposals) if distance_metrics is None: distance_metrics = np.ones((num_proposals,)) * float('inf') assigned_gt_inds = np.concatenate([ np.full( num_proposals // num_gt, i, dtype=np.int32) if i != num_gt -1 else np.full( num_proposals // num_gt + num_proposals % num_gt, i, dtype=np.int32) for i in range(num_gt) ]) assigned_labels = np.concatenate([ np.full(num_proposals // num_gt + num_proposals % num_gt, i, dtype=np.int32) for i in range(num_gt) ]) # Normalize distances to [0,1] range where lower distance means higher weight normalized_distances = (distance_metrics - distance_metrics.min()) / (distance_metrics.max() - distance_metrics.min()) pos_weight = ( np.concatenate([ normalized_distances[num_proposals // num_gt * i : (num_proposals // num_gt * (i+1)) + (i == num_gt -1) * (num_proposals % num_gt)] * (1.0 - float(i +1) / float(num_gt +1)) for i in range(num_gt) ]) if num_proposals > num_gt else normalized_distances * np.ones_like(assigned_gt_inds)) return { 'assigned_gt_inds': assigned_gt_inds, 'assigned_labels': assigned_labels, 'pos_weight': pos_weight } Now `pos_weight` will be influenced by `distance_metrics`, giving more control over how weights are adjusted based on distances.<|end|> <|user|>_Thanks! Now make it handle multi-class assignments.<|end|> <|assistant|>_Sure thing! To handle multi-class assignments, we'll modify `_get_assign_result` so that each proposal can belong to multiple classes with different probabilities. Here’s how you can do it: python def _get_assign_result(gt_kps=_get_gt_kps(), proposals=_get_proposals(), distance_metrics=None, class_probabilities=None): import numpy as np # Default class probabilities if not provided; assume uniform distribution over classes. if class_probabilities is None: class_probabilities = {i: [1/len(gt_kps)]*len(gt_kps) for i in range(len(proposals))} # Ensure class_probabilities is provided as a dictionary with keys as proposal indices. assert isinstance(class_probabilities, dict), "class_probabilities must be a dictionary" # Generate default distance metrics if not provided. if distance_metrics is None: distance_metrics = {i: [float('inf')]*len(gt_kps) for i in range(len(proposals))} assert isinstance(distance_metrics, dict), "distance_metrics must be a dictionary" num_classes = len(set([key for sublist in class_probabilities.values() for key in sublist])) assigned_classes_list = [] assigned_weights_list = [] for prop_idx in range(len(proposals)): prob_dist_for_this_proposal = class_probabilities[prop_idx] distances_for_this_proposal = distance_metrics[prop_idx] normalized_distances_for_this_proposal = (distances_for_this_proposal - min(distances_for_this_proposal)) / (max(distances_for_this_proposal) - min(distances_for_this_proposal)) for cls_idx, prob_val in enumerate(prob_dist_for_this_proposal): weight_for_class_and_instance = prob_val * (1 - normalized_distances_for_this_proposal[i]) / sum(prob_dist_for_this_proposal) assigned_classes_list.append(cls_idx) assigned_weights_list.append(weight_for_class_and_instance) return { 'assigned_classes': assigned_classes_list, 'weights': assigned_weights_list } In this modification: - We added an additional parameter `class_probabilities`, which should be a dictionary mapping each proposal index to its probability distribution over classes. - We also ensured that `distance_metrics` is provided as a dictionary mapping each proposal index to its distance metrics array. - The function now calculates weights dynamically based on both class probabilities and normalized distances. This should give you more control over multi-class assignments using both class probabilities and distance metrics.<|end|> <>: Hi there! I'm working with this code snippet that defines a `ZarrWrapper` class designed to wrap Zarr groups into datasets using xarray structures. It seems quite complex with its custom methods like `__getitem__`, `_parse_slice`, `_normalize_key`, `_normalize_key_part`, and `_open_array`. Here's the snippet: python class ZarrWrapper(xarray.Dataset): # TODO add documentation here! # TODO add link here! # TODO parameterize chunk? # TODO double check what happens with nested groups! # TODO should we handle multiple metadata objects? what about hard-to-merge things like attributes? some