Note in Figure 7.12a that no participant had a touch-typing speed in the range of 60 wpm. Even so, we might conjecture that a user with a touch-typing speed of 60 wpm would have a stylus-tapping speed of: (3)y=0.1342(60)+15.037=23.1 wpm Given the scatter of points in ...
The symmetric-range accuracy A of a sampler is defined as the fractional range, symmetric about the true concentration, that includes a specified proportion of sampler measurements. In this article, we give an explicit expression for A assuming that the sampler measurements follow a one-way random...
@interfaceAAMoveOverEventMessageModel:NSObject@property(nonatomic,copy)NSString*name;@property(nonatomic,strong)NSNumber*x;@property(nonatomic,strong)NSNumber*y;@property(nonatomic,copy)NSString*category;@property(nonatomic,strong)NSDictionary*offset;@property(nonatomic,assign)NSUIntegerindex;@end ...
reshape = TRUE) partial_dep(fit, v = "Petal.Length", X = X_train, reshape = TRUE) |> plot(show_points = FALSE) ice(fit, v = "Petal.Length", X = X_train, reshape = TRUE) |> plot(alpha = 0.05) perm_importance( fit, X = X_valid, y = y_valid, loss = "mlogloss", res...
Python fromsklearn.metricsimportroc_curve fpr, tpr, _ = roc_curve(test_y, probabilities[:,1]) plt.plot(fpr, tpr) plt.plot([0,1], [0,1], color='grey', lw=1, linestyle='--') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') ...
container interfaces { list interface { key "name"; leaf name { type string; } leaf status { type boolean; default "true"; } leaf observed-speed { type yang:gauge64; units "bits/second"; config false; } } }Built-in Types Similar to many programming languages, YANG has a set of bui...
"a11yhelp,codemirror,magicline,scayt,showborders", "superimageImageMaxSize": 5, "disallowedContent": "form[action]; *[formaction]; script; *[on*]", "linkTargets": ["notSet", "_blank"], }, "attachmentEntity": { "name": "msdyn_richtextfiles", "fileAttributeName": "msdyn_fileblob...
Environment: conda create -n bug python=3.10 -y conda activate bug conda install pytorch==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia pip install transformers==4.36.2 datasets==2.16.1 peft==0.9.0 accelerate==0.25.0 A minimal example: ...
nsys profile -s none -t nvtx,cuda -o <path/to/profiling/output> --force-overwrite true \ --capture-range=cudaProfilerApi --capture-range-end=stop python $TRAIN_PATH/deepy.py \ $TRAIN_PATH/train.py --conf_dir configs <config files> ...
a The dependencies of the observed variables y and hidden states x. b The transition probabilities between the hidden states x. Full size image To estimate the most likely state at each time point, 3 probabilities are needed. First, we need to determine the probability of an observation given...