Overview del Torneo de Tenis
El enfrentamiento entre Joshua & Charlton contra Park & Yong Joon promete ser uno de los más emocionantes del calendario del 2025. Estos equipos, conocidos por su astucia estratégica y técnica en cancha, han mostrado un impresionante historial de partidos anteriores, haciendo de este choque un evento imperdible para los entusiastas del tenis.
Joshua & Charlton, con su excepcional coordinación y habilidades defensivas, son favoritos por algunos expertos debido a su capacidad para mantener la calma bajo presión. Por otro lado, Park & Yong Joon han demostrado ser formidables ofensivos, conocidos por sus tiros agresivos y rápidos cambios de ritmo, lo que les ha permitido destacarse en partidos anteriores.
Este encuentro no solo es un duelo de habilidades y estrategias, sino también una batalla de resistencia física y mental, factores cruciales para determinar el resultado en partidos de alto nivel.
Joshua, Charlton
Park, Yong Joon
Predictions:
| Market | Prediction | Odd | Result |
|---|
Predictions y Análisis
Probable Winner
La tendencia actual y el rendimiento en partidos recientes sugieren que Joshua & Charlton tienen una ligera ventaja. Su experiencia en torneos internacionales y su habilidad para manejar la presión podrían darles la delantera en este emocionante partido.
Total de sets
Dado el estilo defensivo de Joshua & Charlton y el ataque agresivo de Park & Yong Joon, se espera un total de sets cercano a 5. Ambas parejas tienen la capacidad de ganar fácilmente dos sets, lo que podría llevar a un tercer tie-break decisivo.
First Set Winner
Se anticipa que Park & Yong Joon podrían sorprender al comenzar fuerte, llevándose el primer set. Su capacidad para empezar los partidos con un ataque veloz puede darles una ventaja temprana, aunque no asegura la victoria del partido.
Set Betting Insights
En los juegos de sets individuales, se observa que las parejas tienden a oscilar entre el control defensivo y el ataque. Será crucial el desempeño de los servicios y la recepción para dominar los sets. Se espera que los momentos de quiebre sean esenciales para definir a los ganadores de cada set.
Match Betting Highlights
En términos de apuestas generales de match, se recomienda considerar el potencial de la partida a tres sets completos, lo que representa una oportunidad de alta rentabilidad para los apostadores más osados. La resistencia física y la adaptación estratégica durante el partido serán factores determinantes.
[0]: «»»
[1]: Copyright (c) 2018 Intel Corporation
[2]: Licensed under the Apache License, Version 2.0 (the «License»);
[3]: you may not use this file except in compliance with the License.
[4]: You may obtain a copy of the License at
[5]: http://www.apache.org/licenses/LICENSE-2.0
[6]: Unless required by applicable law or agreed to in writing, software
[7]: distributed under the License is distributed on an «AS IS» BASIS,
[8]: WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
[9]: See the License for the specific language governing permissions and
[10]: limitations under the License.
[11]: «»»
[12]: import logging as log
[13]: import numpy as np
[14]: import warnings
[15]: from mo.graph.graph import Node, Graph
[16]: from mo.ops.op import Op
[17]: from mo.utils.error import Error
[18]: from mo.utils.rename import rename_nodes
[19]: from extensions.ops.const import Const
[20]: class Reshape(Op):
[21]: op = ‘Reshape’
[22]: def __init__(self, graph: Graph, attrs: dict):
[23]: mandatory_props = {
[24]: ‘type’: self.op,
[25]: ‘op’: self.op,
[26]: ‘version’: ‘opset1’,
[27]: ‘infer’: self.infer,
[28]: ‘reshape_dims’: None
[29]: }
[30]: super().__init__(graph, mandatory_props, attrs)
[31]: def backend_attrs(self):
[32]: return [
[33]: ‘new_shape’,
[34]: ‘old_shape’,
[35]: ‘reshape_dims’,
[36]: ]
[37]: @staticmethod
[38]: def infer(node: Node):
[39]: node_name = node.soft_get(‘name’, node.id)
[40]: connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
[41]: assert len(connected_in_ports) == 1 or len(connected_in_ports) == 2,
[42]: «Reshape operation expect one or two inputs, node: `{}` has {} inputs».format(node_name,
[43]: len(connected_in_ports))
[44]: input_value = node.in_port(0).get_source().get_value()
[45]: if input_value is not None:
[46]: input_shape = input_value.shape
[47]: else:
[48]: input_shape = node.in_port(0).data.get_shape()
[49]: _shape = node.in_port(1).data.get_value()
[50]: if _shape is None:
[51]: raise Error(«Reshape operation second input (shape) must be defined (available)»)
[52]: if isinstance(_shape, np.ndarray):
[53]: shape = _shape.flatten().tolist()
[54]: else:
[55]: shape = _shape
[56]: # convert shape info to int64 if it is int32/int16/int8
[57]: if shape and isinstance(shape, (list, np.ndarray)):
[58]: if all(isinstance(item, int) for item in shape):
[59]: shape = [np.int64(item) for item in shape]
[60]: else:
[61]: for i in range(len(shape)):
[62]: if shape[i] < 0:
[63]: shape[i] = np.int64(shape[i])
[64]: else:
[65]: shape[i] = int(shape[i])
[66]: node.reshape_dims = shape
[67]: # calculate output shape
[68]: output_shape = list()
[69]: negative_axes = list()
[70]: inferred_shape = list()
[71]: if -1 in shape:
[72]: assert shape.count(-1) <= 1, "flip-flops are not allowed in reshape operation"
[73]: for dim in range(len(shape)):
[74]: if shape[dim] [-1]
:param graph: graph to apply Reshape into two ops Convert data type and Reshape on
:param node: Reshape node to apply transform on
«»»
# check case for split into two ops Convert data type and Reshape
reshaped_dims_list = np.array(node.reshape_dims).tolist()
first_dim_was_inferred_before = False
first_reshape_dim_replaced_to_one_dim = False
first_dim = reshaped_dims_list.pop(0)
if first_dim == -1:
first_reshape_dim_replaced_to_one_dim = True
prev_node = node.in_node(node.in_port(1).get_source().get_source().get_source().get_source())
if prev_node.soft_get(‘type’, None) == ‘Unsqueeze’:
new_shape = np.array(prev_node.shape).tolist() + [1]
new_node_const = Const(graph, {‘value’: new_shape}).create_node()
new_node_const.out_port(0).connect(node.in_port(1).get_source().get_source().get_source().get_source().in_port(0))
G.insert_node(new_node_const)
rename_nodes([(prev_node.id, prev_node.id + ‘/old’)])
prev_node.shape = new_shape
prev_node.in_port(0).disconnect()
prev_node.in_port(0).connect(new_node_const.out_port(0))
G.delete_node(prev_node.id + ‘/old’)
elif isinstance(first_dim, np.int64):
first_dim_was_inferred_before = True
if first_dim == 1:
first_reshape_dim_replaced_to_one_dim = True
for in_edge in G.in_edges(node.id):
reshape_dist_node = in_edge.from_node
if len(G.out_edges(reshape_dist_node.id)) > 1:
if reshape_dist_node.has_valid(‘type’) and reshape_dist_node.type == ‘Reshape’:
continue
if reshape_dist_node.has_valid(‘type’) and reshape_dist_node.type == ‘Unsqueeze’:
if has_incoming_const_input(graph, reshape_dist_node): # value for unsqueeze has been generated by Constant op
data = np.array(reshape_dist_node.shape).tolist() + [1]
new_node_const = Const(graph, {‘value’: data}).create_node()
new_node_const.out_port(0).connect(reshape_dist_node.in_port(0))
G.insert_node(new_node_const)
rename_nodes([(reshape_dist_node.id, reshape_dist_node.id + ‘/old’)])
G.delete_node(new_node_const.out_port(0).get_connection().get_source().node)
reshape_dist_node.shape = np.array(reshape_dist_node.shape).tolist() + [1]
reshape_dist_node.in_port(0).disconnect()
reshape_dist_node.in_port(0).connect(new_node_const.out_port(0))
G.delete_node(reshape_dist_node.id + ‘/old’)
else:
log.warning(‘Can’t handle Unsqueeze node for node {}’.format(node.name))
data = np.array(reshape_dist_node.out_port(0).data.get_shape()).tolist() + [1]
new_reshape_dims = np.array(node.reshape_dims)
new_reshape_dims = np.insert(new_reshape_dims, 0, 1)
node.reshape_dims = new_reshape_dims.tolist()
new_reshaped_dims_list = np.array(new_reshape_dims).tolist()
new_reshaped_value = np.array(data).tolist()
out_data = np.full(new_data.tolist(), None, dtype=np.object)
out_data[node.in_port(1).get_source().node_id] = new_reshaped_value
new_data = infer_shapes(out_data)
node.in_port(1).data.set_value(np.array(new_data[node.in_port(1).get_source().node_id], dtype=np.int64))
log.debug(‘Reshape operation {} can be splited into two operations Convert data type and Reshape’.format(node.name))
if first_reshape_dim_replaced_to_one_dim:
new_reshaped_dims_list.insert(0, -1)
# Create new reshaped dims for operation
new_reshaped_dims = np.array(new_reshaped_dims_list)
new_reshaped_dims[np.where(new_reshaped_dims == -1)] = np.prod(input_shape) / np.prod(new_reshaped_dims[np.where(new_reshaped_dims != -1)])
# Create new value for second input by using original value multiplied by new inferred first dimension
final_value = np.multiply(np.array(node.old_shape), new_reshaped_dims)[np.newaxis]
if len(final_value.shape) > 1:
final_value = final_value.flatten()
# Update graph shape info with new value
if final_value.size == 1:
final_value.resize(final_value.shape[0])
out_data = np.full(data_dict.keys(), None, dtype=np.object)
out_data[node.in_port(1).get_source().node_id] = final_value.tolist()
new_data = infer_shapes(out_data)
node.in_port(1).data.set_value(np.array(new_data[node.in_port(1).get_source().node_id], dtype=np.int64))
# in some cases inference fails because information about input data type was lost
# this code restores it
if node.has_valid(‘out_data_type’) and node.out_port(0).data.get_value() is not None
and node.out_port(0).data.get_value() is not None:
# propagate input data type information to unknown data types in reshape outputs
data_type = node.out_data_type.numpy()
unknown_types_indices = np.where(node.out_port(0).data.get_value() == 0)[0]
if len(unknown_types_indices) > 0:
log.debug(‘node «{}» output ports has unknown data types’.format(node.name))
for idx in unknown_types_indices:
node.out_port(0).data.set_type(data_type[idx], idx)
data_value = node.out_port(0).data.get_value()
unknown_types_indices = np.where(data_value == 0)[0]
if len(unknown_types_indices) > 0:
log.debug(‘node «{}» out port value {} has unknown data types’.format(node.name,
node.out_port(0).data.get_source().node.name))
values_to_set = []
for idx in unknown_types_indices:
values_to_set.append(data_type[idx])
log.info(‘setting node «{}» out port value {} types to {}’.format(node.name,
node.out_port(0).data.get_source().node.name,
values_to_set))
if node.out_port(0).data.get_source().node.has_valid(‘value’):
values = node.out_port(0).data.get_source().node.value
values_to_set = np.array(values_to_set)
values[np.where(values == 0)] = values_to_set
node.out_port(0).data.get_source().node.value = values.tolist()
def has_incoming_const_input(graph: Graph, node: Node):
«»»
check if among the single incoming edges there is one with Constant or Const as source
:param graph: graph instance
:param node: operation to check constant inputs
