Over 63.5 Goals handball predictions tomorrow (2025-12-21)

Possibili Svolgimenti delle Partite

Come si svilupperanno le partite? Ecco alcune possibili scenari che potrebbero verificarsi durante i match:

• Scontro Diretto tra Giovanni Rossi ed il portiere avversario nel primo tempo: Potrebbe essere decisivo per stabilire il ritmo della partita tra il Team A ed il suo avversario.
• L'influenza del pubblico suggerisce che ci saranno momenti altamente emozionanti quando Mario Bianchi si presenterà davanti alla porta avversaria durante le fasi calde della gara del suo team contro il rivale diretto.
• Federico Verdi potrebbe dimostrare ancora una volta perché è uno dei migliori portieri della competizione quando affronterà l'attacco temibile dell'avversario nel match cruciale tra il suo team ed il contendente numero uno al titolo finale.

Riepilogo dei Pronostici Esperti

Riassumendo tutti i fattori analizzati, ecco i nostri pronostici definitivi per domani:

• Nel primo incontro tra il forte attacco del Team A ed una difesa meno solida dell'avversario ci aspettiamo più di 63.5 goal complessivi grazie alle capacità offensive già dimostrate nella stagione corrente da entrambe le formazioni coinvolte in questa sfida importante.# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License Version 2.0 (the "License"). You may not use # this file except in compliance with the License. A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR # CONDITIONS OF ANY KIND, express or implied. See the License for the # specific language governing permissions and limitations under the License. import logging import numpy as np from smdebug.core.collection import CollectionConfig from smdebug.core.json_config import json_config from smdebug.profiler.utils import get_file_path from smdebug.trials import create_trial_from_config_dict logger = logging.getLogger(__name__) def _get_all_step_metadata(trial): step_metadata = {} for tensor_name in trial.tensor_names(): step_metadata[tensor_name] = set(trial.tensor(tensor_name).steps()) if len(step_metadata[tensor_name]) == 0: logger.info("There are no steps recorded for tensor %s", tensor_name) continue if min(step_metadata[tensor_name]) != max(step_metadata[tensor_name]) + 1: logger.warning( "The steps for tensor %s are not contiguous", tensor_name, ) if min(step_metadata[tensor_name]) != min( [min(x) for x in step_metadata.values() if len(x) > 0] ): logger.warning( "The first step recorded for tensor %s is not the same as the first step recorded for other tensors", tensor_name, ) # Add step zero if it's missing since we want to visualize it. if min(step_metadata[tensor_name]) > 0: step_metadata[tensor_name].add(0) step_metadata[tensor_name] = sorted(list(step_metadata[tensor_name])) step_metadata[tensor_name].append( max(step_metadata[tensor_name]) + 1 ) # Add one more than last to make sure we include all data # If there are any steps that have no data recorded then add them. # For example when using DistributedDataParallel we only record from rank zero but we need to record all steps. # Also need to do this when using a profiler hook because profiler hook only records data from every Nth step. for step_num in range(min(step_metadata[tensor_name]), max(step_metadata[tensor_name])): if step_num not in step_metadata[tensor_name]: step_metadata[tensor_name].append(step_num) logger.info( "Tensor %s does not have data recorded for step %d so adding it", tensor_name, step_num, ) assert ( len(step_metadata[tensor_name]) == max(step_metadata[tensorName]) - min(step_metadata[tensorName]) + 1 ) return ( min([min(x) for x in step_metadata.values() if len(x) > 0]), max([max(x) for x in step_metadata.values() if len(x) > 0]), step_metadata, ) def _get_default_min_max_step_from_config(config): return config.get("min_step", None), config.get("max_step", None) def _get_min_max_step_from_args(args): return args.min_step or args.max_step or None def _get_min_max_step_from_config_and_args(config_dict, args): min_max_steps = _get_default_min_max_step_from_config(config_dict) min_max_steps_from_args = _get_min_max_step_from_args(args) if min_max_steps_from_args: return min_max_steps_from_args return min_max_steps def get_min_max_step( trial: create_trial_from_config_dict, ) -> tuple[int | None, int | None]: """ Get min and max steps based on command line arguments and config. Args: trial (Trial): The trial object. Returns: tuple[int | None]: Minimum and maximum steps to be plotted. """ config_dict = trial.config._config_dict # If command line args are specified then use them else use config dict. min_max_steps = _get_min_max_step_from_config_and_args(config_dict, trial.args) # If no command line args or config dict values specified then use all available data. if not any(min_max_steps): _, _, metadata = _get_all_step_metadata(trial) return metadata[0], metadata[1] # If only one of them is specified then use that value and find the other value from available data. if not min_max_steps[0]: _, max_step = _get_all_step_metadata(trial)[1:] return metadata[0], max_step elif not min_max_steps[1]: min_step, _ = _get_all_step_metadata(trial)[0:2] return min_step, metadata[1] # If both are specified then return them as they are. return min_max_steps def get_full_tensor_data( trial: create_trial_from_config_dict, tensor_names: list[str], tag: str, collection_configs: list[CollectionConfig], min_step=None, max_step=None, ) -> dict[str,list[int]]: """ Get full data for each of the given tensor names. Args: trial (Trial): The trial object. tensor_names (list[str]): List of tensor names to get data for. tag (str): The tag associated with the tensors. collection_configs (list[CollectionConfig]): List of collection configs. min_step (int): Minimum step number to start plotting from. max_step (int): Maximum step number to plot up to. Returns: dict[str,list[int]]: Dictionary containing full data for each of the given tensor names. """ def __get_tensor_data( trial, tensor_names, tag, collection_configs, min_step=None, max_step=None, ): """ Get full data for each of the given tensor names. Args: trial (Trial): The trial object. tensor_names (list[str]): List of tensor names to get data for. tag (str): The tag associated with the tensors. collection_configs (list[CollectionConfig]): List of collection configs. min_step (int): Minimum step number to start plotting from. max_step (int): Maximum step number to plot up to. Returns: dict[str,list[int]]: Dictionary containing full data for each of the given tensor names. """ def __check_if_tensor_in_collection_configs(tensor_names): """ Check if any of the given tensor names are present in collection configs. Args: tensor_names (list[str]): List of tensor names to check. Returns: bool: True if any of the given tensor names are present in collection configs else False. """ for name in tensor_names: for collection_config in collection_configs: if name == collection_config.name or name.endswith(collection_config.name): return True return False # If no collections are present then just get all data as there will be no aggregation to do later on if not __check_if_tensor_in_collection_configs(tensor_names): full_data = {} for name in tensor_names: full_data[name] = [] try: if tag == "profiler": full_data[name] = json_config.get_tensor_data( get_file_path(trial=trial), name=name, tag=tag, minStep=min_step, maxStep=max_step, ) else: full_data[name] = json_config.get