Nedbank Cup stats & predictions

Tendenze Attuali nel Calcio Sudafricano

I trend nel calcio sudafricano stanno evolvendo rapidamente grazie all'introduzione di nuove tecnologie e strategie innovative. Ecco alcune tendenze attuali da tenere d'occhio:

• Data Analytics:** Le squadre stanno sempre più utilizzando l'analisi dei dati per ottimizzare le loro prestazioni sia in fase offensiva che difensiva.
• Tecnologia GPS:** L'utilizzo dei dispositivi GPS durante gli allenamenti permette alle squadre di monitorare costantemente la condizione fisica dei giocatori.
• Fitness Personalizzato:** Programmi di fitness personalizzati stanno diventando comuni tra i giocatori professionisti per massimizzare l'efficienza atletica individuale.
• <**<|vq_1081|>[0]: # -*- coding: utf-8 -*- [1]: import numpy as np [2]: import scipy.stats as stats [3]: import scipy.special as special [4]: from itertools import product [5]: from sklearn.base import BaseEstimator [6]: from sklearn.utils.validation import check_X_y [7]: class BayesianNetwork(object): [8]: """ [9]: A class for Bayesian Networks. [10]: Parameters [11]: ---------- [12]: structure : tuple of tuples [13]: Each tuple represents a variable and its parents. [14]: The first element is the index of the variable and the rest are the indices of its parents. [15]: For example: ((0,), (1,), (1,), (0,), (0,)) defines a network with three variables: [16]: node_0 depends on nothing and node_1 and node_2 depend on node_1, [17]: while node_3 depends on node_0 and node_4 depends on node_0. [18]: Attributes [19]: ---------- [20]: n_variables : int [21]: Number of variables. [22]: n_features : int [23]: Number of features (= number of states of each variable). [24]: structure : tuple of tuples [25]: Each tuple represents a variable and its parents. [26]: The first element is the index of the variable and the rest are the indices of its parents. [27]: cpd_tables : list of ndarray [28]: Conditional probability tables for each variable. [29]: Methods [30]: ------- [31]: fit(X) [32]: Fits the CPD tables to X using Maximum Likelihood Estimation. [33]: predict(X) [34]: Predicts the missing values given other observed values using Gibbs sampling. [35]: score(X) [36]: Returns the log-likelihood of X under this model. [37]: Example [38]: ------- [39]: >>> X = np.array([[0., np.nan], [np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]]) [40]: >>> structure = ((0,), (1,)) [41]: >>> bn = BayesianNetwork(structure) [42]: >>> bn.fit(X) [43]: >>> bn.predict(X) """ class BayesianNetwork(BaseEstimator): """ """ def __init__(self, structure): """ Parameters ---------- structure : tuple of tuples Each tuple represents a variable and its parents. The first element is the index of the variable and the rest are the indices of its parents. For example: ((0,), (1,), (1,), (0,), (0,)) defines a network with three variables: node_0 depends on nothing and node_1 and node_2 depend on node_1, while node_3 depends on node_0 and node_4 depends on node_0. """ self.structure = structure self._check_structure() self.n_variables = len(set(sum(self.structure, ()))) self.n_features = None self.cpd_tables = None def _check_structure(self): """ """ if not isinstance(self.structure, tuple): raise ValueError("Structure must be a tuple.") if not all([isinstance(elems, tuple) for elems in self.structure]): raise ValueError("Each element must be a tuple.") if not all([len(elems) >= 1 for elems in self.structure]): raise ValueError("Each element must have at least one element.") if not all([isinstance(elems[-1], int) for elems in self.structure]): raise ValueError("The last element must be an integer.") if not all([all([isinstance(elems[i], int) for i in range(len(elems)-1)]) for elems in self.structure]): raise ValueError("All elements except for the last one must be integers.") if not all([elems[-1] >= len(elems)-1 for elems in self.structure]): raise ValueError("The last element must be greater than or equal to the number of elements minus one.") def _check_input(self, X): """ """ X = check_X_y(X) if not isinstance(X, np.ndarray): raise ValueError("X must be an ndarray.") if not X.ndim == 2: raise ValueError("X must be two-dimensional.") if not X.shape[-1] == self.n_variables: raise ValueError("X must have as many columns as there are variables.") if not issubclass(X.dtype.type, np.number): raise ValueError("X can only contain numbers.") return X def fit(self, X): """ Fits the CPD tables to X using Maximum Likelihood Estimation. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples,n_features] Training data where n_samples is the number of samples and n_features is the number of features. Returns self : object """ X = self._check_input(X) self.n_features = len(np.unique(X)) counts = [] for var_idx in range(self.n_variables): var_parent_indices = [elem[-1] for elem in self.structure if elem[:-1] == (var_idx,)] var_parents_cardinalities = [self.n_features] * len(var_parent_indices) counts.append(np.zeros(tuple(var_parents_cardinalities + [self.n_features]))) count_row_idxs = [] for sample_idx in range(X.shape[-1]): sample_var_parents = tuple(X[sample_idx][parent_idx] for parent_idx in var_parent_indices) sample_var_value = int(X[sample_idx][var_idx]) count_row_idxs.append(sample_var_parents + (sample_var_value,)) counts[var_idx][count_row_idxs] += 1 cpd_tables = [] for var_idx in range(self.n_variables): var_parent_indices = [elem[-1] for elem in self.structure if elem