Saudi Pro League stats & predictions

Daily Betting Predictions: Your Guide to Winning Bets

Betting on football can be an exhilarating experience when done right. Our expert betting predictions are designed to give you an edge over others by providing detailed analyses based on statistical models and expert insights. Here's what you can expect from our daily betting predictions:

Prediction Format

• Morning Briefings: Receive comprehensive previews before each matchday begins, covering team news, tactical setups, and key matchups.
• Lunchtime Updates: Get mid-day updates that highlight any last-minute changes or developments that could impact match outcomes.
• Eve-of-Match Analysis: Just before kickoff time, enjoy an in-depth analysis that includes predicted line-ups, formation strategies, and potential game-changers.
• Late Betting Tips: As matches unfold live throughout the day or evening (depending on timing), receive late betting tips based on current performance trends.KabirNidhi/GraphSAGE<|file_sep|>/src/graphsage/clustered_sampler.py # Copyright (c) 2020-present NAVER Corp. # MIT License import logging import numpy as np import torch from graphsage.sampler import NeighborSampler from graphsage.utils import ( get_device, ) logger = logging.getLogger(__name__) class ClusteredSampler(NeighborSampler): def __init__( self, num_neighbors, max_num_hops, graph, batch_size, num_clusters, shuffle=False, num_workers=0, add_self_loop=True, prefetch=False, **kwargs ): super(ClusteredSampler).__init__( num_neighbors=num_neighbors, max_num_hops=max_num_hops, graph=graph, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, add_self_loop=add_self_loop, prefetch=prefetch, **kwargs ) self.num_clusters = num_clusters if self.graph.is_homogeneous: self.clusters = self._get_cluster( self.graph.nodes[self.graph.train_nid], self.num_clusters, device=get_device() ) logger.info('Cluster size: %d' % len(self.clusters)) else: self.clusters = { ntype: self._get_cluster( nodes=self.graph.nodes[ntype][self.graph.train_nid[ntype]], num_clusters=self.num_clusters[ntype], device=get_device() ) for ntype in self.graph.ntypes } logger.info('Cluster size: %s' % str({k: len(v) for k,v in self.clusters.items()})) if self.shuffle: np.random.shuffle(self.clusters) self.cur_idx = 0 def _get_cluster(self, nodes=None, num_clusters=None, device='cpu', ): if nodes is None: nodes = self.graph.nodes() # get node degree deg = self.graph.indegree() if isinstance(deg[0], dict): deg = {ntype: np.array(list(deg_dict.values())) for ntype,dg_dict in deg.items()} # cluster nodes by degree clusters = {} if isinstance(deg[0], dict): # heterogeneous graph # use node type-specific degree bins for ntype,dg_array in deg.items(): min_dg = dg_array.min().item() max_dg = dg_array.max().item() # compute degree bins step = (max_dg - min_dg) / num_clusters[ntype] bins = [min_dg + i * step for i in range(num_clusters[ntype] + 1)] bins[-1] += 0.0001 # assign nodes into clusters cluster_id = np.digitize(dg_array.cpu(), bins=bins) clusters[ntype] = [torch.as_tensor(nodes[cluster_id == i], dtype=torch.long).to(device) for i in range(num_clusters[ntype])] logger.info(f'{ntype}: min_degree={min_dg}, max_degree={max_dg}') else: else: else: else: else: else: else: else: else: else: else: else: else: else: else: if isinstance(deg[0], dict): for ntype,dg_array in deg.items(): min_dg = dg_array.min().item() max_dg = dg_array.max().item() # compute degree bins step = (max_dg - min_dg) / num_clusters[ntype] bins = [min_dg + i * step for i in range(num_clusters[ntype] + 1)] bins[-1]