Y = pd.Series(Y) # counts the occurrences of each value freq_of_Y_values = Y.value_counts() Prob_of_Y_values = freq_of_Y_values/Y.shape[0] Prob_of_Y_values.sort_index().plot(kind='bar') plt.grid(); 这是输出:图8.6:Y 的概率分布得到5 个正面的概率约为 0.25,所以大约 25%的...
VaR3=calculate_bootstrap_VaR(returns) print(f"使用Bootstrap法,95%的置信水平的五日价值在险(VaR)为:{VaR3:.4f}") 使用Bootstrap法,95%的置信水平的五日价值在险(VaR)为:-0.0642 04 蒙特卡洛模拟 蒙特卡洛模拟是一种通过模拟随机过程来估计某个变量的分布的方法。因此,蒙特卡洛方法通常涉及到根据某种概率...
(name='max_cycle'), left_on='unit_nb', right_index=True) # Calculate remaining useful life for each row remaining_useful_life = result_frame["max_cycle"] - result_frame["time_cycle"] result_frame["RUL"] = remaining_useful_life # drop max_cycle as it's no longer needed result_...
(norm_X),norm_X) #Calculate the eigenvectors and eigenvalues eig_val, eig_vec = np.linalg.eig(scatter_matrix) eig_pairs = [(np.abs(eig_val[i]), eig_vec[:,i]) for i in range(n_features)] # sort eig_vec based on eig_val from highest to lowest eig_pairs.sort(reverse=True) ...
data was drawn from a normal distribution. Parameters --- x : array_like Array of sample data. Returns --- W : float The test statistic. p-value : float The p-value for the hypothesis test. 1. 2. 3. 4. 5. 6. 7. 8.
因子的衡量标准(fitness function)或者信息系数(Information Coefficient,简称IC)可以是以下任何一个:pearson corr , R^2, spearman corr, weighted corr, mse, rmse, entropy, gini, accuracy, recall, precision, f1 score。但是要符合一定的前提假设,比如常用的pearson correlation(后文的IC都假设为它) 公式本来就...
循环神经网络,然后hui用Theano(一个可以在GPU上进行运算的库)对实现进行优化。我会跳过一些对理解循环神经网络不是很重要的代码片段,但完整的代码可以在这里找到。 语言模型 这里的目标是用RNN构建一个语言模型,下面会解释一下什么是语言模型。假设有一个包含 ...
[excluded[i]] || [], file)) { valid = false; break; } @@ -584,6 +610,9 @@ var Search = { // if we have still a valid result we can add it to the result list if (valid) { + // select one (max) score for the file. + // for better ranking, we should calculate...
any(defect_rate > max_defect_rate): violated_constraints += 1 # Calculate the fitness value based on the objectives and constraints profit = np.sum(np.dot(self.data["production_outputs"].reshape(1, -1), x)) - np.sum(np.dot(self.data["production_costs"].reshape(1, -1), x)) if...
from mlfromscratch.utils import Plot, accuracy_score class NaiveBayes(): """The Gaussian Naive Bayes classifier. """ def fit(self, X, y): self.X, self.y = X, y self.classes = np.unique(y) self.parameters = [] # Calculate the mean and variance of each feature for each class ...