semilog用法 1. 变量命名。 更具描述性:避免使用过于简单通用的变量名,如`x`和`y`。如果`x`代表时间,可以命名为`time_values`;如果`y`代表温度,可以命名为`temperature_readings`。 符合习惯:遵循Python的命名习惯,对于多个单词组成的变量名,使用下划线命名法,如`data_points`而不是`dataPoints`(这是驼峰命名...
Subsequently, we normalize the data to a library size of 10,000 per cell and apply a log1p transformation. Low-quality samples with fewer than 1000 cells are removed, with an exception made for the hamster dataset to preserve an adequate sample size. The data matrix is then refined to ...
(kernel="rbf",probability=True),predict_from_probabilities=True)# RBF SVMssmodel.fit(X,ys)print"CPLE semi-supervised RBF SVM score",ssmodel.score(X,ytrue)# supervised log.reg. score 0.410256410256# self-learning log.reg. score 0.461538461538# semi-supervised log.reg. score 0.615384615385# ...
The time complexity of Rabin-Miller is O(k log3 n) where k is the number of trials and n is the size of the integer under test. The accu- racy of the test increases with the number of trials k. The deterministic primality test referred to as AKS [1], has complexity of AKS is ...
Embryonal carcinoma (EC), characterized by a high degree of stemness similar to that of embryonic stem cells, is the most malignant subtype within non-seminomatous testicular germ cell tumors (TGCTs). However, the mechanisms underlying its malignancy rem
CUDA_VISIBLE_DEVICES=0 python3 train_cifar.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=2000 --epoch_decay_start=1500 --aug_flip=True --aug_trans=True --dataset_seed=1 CUDA_VISIBLE_DEVICES=0 python3 test_cifar.py --dataset=cifar10...
For these 4 collections of datasets, the preprocessed data (low quality cell filtered, raw and log-normalized counts as well as original cell type annotations) obtained from the previous studies were directly analysed with the ‘scib’ pipeline. Integration procedure We performed the 4 integration ...
(2) (1) V (D, G) =Ex∼pdata(x)[log D(x)] + Ez∼pz(z)[log(1 − D(G(z)))], (2) G presents the function performed by generator while D refers to the function of discriminator. Various exten- sions are added to the discriminator in semi-supervised learning to consider...
utils.vis_utils import plot_model from keras import backend # custom activation function def custom_activation(output): logexpsum = backend.sum(backend.exp(output), axis=-1, keepdims=True) result = logexpsum / (logexpsum + 1.0) return result # define the standalone supervised and ...
b comparison of the hazard factor values with different hazard intensities, where the violin plot indicates the median (middle line), maximum (upper line) and minimum (lower line) hazard factor values, “ws” refers to the intensity of wind shear, and “turb” stands for the turbulence intens...