#Sidplay accuracy update

The update is listed as June 07 release on the Surface Laptop Go 2 Update History page. This page covers both Sidplay versions 1 and 2, Sidplay being an frame based emulation capable of playback on low specification PCs. The new firmware update will improves system performance and stability, improves device stability during reboot scenarios, resolves system bugcheck, and improves display accuracy when auto-brightness is enabled. text_kw dict, default=Noneĭict with keywords passed to call. The result is a program which can load and execute C64 machine code programs which produce music and sound. im_kw dict, default=Noneĭict with keywords passed to call.

Whether or not to add a colorbar to the plot.

ax matplotlib axes, default=NoneĪxes object to plot on. The format specification is ‘d’ or ‘.2g’ whichever is shorter. values_format str, default=Noneįormat specification for values in confusion matrix. In which the last estimator is a classifier. Parameters : estimator estimator instance Plot Confusion Matrix given true and predicted labels.Ĭlassmethod from_estimator ( estimator, X, y, *, labels = None, sample_weight = None, normalize = None, display_labels = None, include_values = True, xticks_rotation = 'horizontal', values_format = None, cmap = 'viridis', ax = None, colorbar = True, im_kw = None, text_kw = None ) ¶ Plot Confusion Matrix given an estimator and some data.įrom_predictions(y_true, y_pred, *) show ()įrom_estimator(estimator, X, y, *) classes_ ) > disp = ConfusionMatrixDisplay ( confusion_matrix = cm. predict ( X_test ) > cm = confusion_matrix ( y_test, predictions, labels = clf. fit ( X_train, y_train ) SVC(random_state=0) > predictions = clf. random_state = 0 ) > clf = SVC ( random_state = 0 ) > clf.

import matplotlib.pyplot as plt > from sklearn.datasets import make_classification > from trics import confusion_matrix, ConfusionMatrixDisplay > from sklearn.model_selection import train_test_split > from sklearn.svm import SVC > X, y = make_classification ( random_state = 0 ) > X_train, X_test, y_train, y_test = train_test_split ( X, y.