We benchmarked MAGPIE against 14 previously published methods and found that MAGPIE outperformed all other methods in both independent test set and several imbalanced orthogonal validation sets. Notably, MAGPIE was able to make predictions on multiple types of exonic mutations, fulfilling 5–60% of ...
in predict_proba(self, data, output_margin, ntree_limit) 471 class_probs = self.booster().predict(test_dmatrix, 472 output_margin=output_margin, --> 473 ntree_limit=ntree_limit) 474 if self.objective == "multi:softprob": 475 return class_probs /usr/local/lib/python2.7/dist-packages/...
The complete demo program, with a few minor edits to save space, is presented inFigure 3. I indent with two spaces rather than the usual four spaces to save space. Note that Python uses the “\” character for line continuation. I used Notepad to edit my program. Most of my colleagues...
This calculation is in line with the initial processing of the CDR data into consistent annual tower sets but has the disadvantage of the resulting proportions potentially underestimating the population at the beginning of the year and overestimating at the end of the year due to general population...
Each test embryo was compared with three reference image sets. The mean of cosine similarities to these reference sets is plotted as a datapoint for each reference image timepoint. Boxplots are based on the distribution of similarity values above 0.8. The center represents the median, box ...
A good way to see where this article is headed is to take a look at the demo program in Figure 1. The demo sets up three Bernoulli machines with probabilities of payout of (0.3, 0.5, 0.7), respectively. In a non-demo scenario, the probabilities are unknown to you, of course. You...
File "C:\Python\PycharmProjects\PSG\MikesDemos\Mike_Desktop_Widget_Drive_Usage.py", line 191, in <module> main(location) File "C:\Python\PycharmProjects\PSG\MikesDemos\Mike_Desktop_Widget_Drive_Usage.py", line 154, in main update_window(window) # sets the progress bars ^^^ File "C:...
loop unrolling to enable vectorization. This optimization transforms a loop so that instead of performing a single operation in the body of the loop on a single set of operands (such as adding two integers stored in different arrays), the same operation would be performed on multiple sets of ...
The “//” operator is integer division in Python. Before calling the program-defined accuracy function, the demo sets the network into eval mode. Technically, this isn’t necessary because train and eval modes only give different results if the network uses dropout or layer batch normalization...
The configurations defined in train_ecapa.yaml are also passed as parameters.The command to run the script to train the model is:python train.py train_ecapa.yaml --device "cpu"In the future, the training script train.py will be designed to work for Intel® GPUs such as th...