![]() ![]() The significant deviation of the UDG model from the real/practical models is substantially limiting the applicability of protocols which are based on UDGs. In practice, however, the UDG model significantly deviates from many real wireless networks, due to many reasons including: multi-path fading, antenna design issues, inaccurate node position estimation, etc. Examples of these protocols include routing, topology control, distributed information storage/retrieval and a great variety of other applications. So far, many protocols have been based on the idealized unit-disk graph (UDG) network model, where two wireless nodes can directly communicate if and only if their physical distance is within a fixed parameter R. A deep understanding of the structural properties of wireless networks is critical for evaluating the performance of network protocols and improving their designs. The connectivity structures of wireless networks exhibit strong correlations with the physical environment due to the signal transmission model of wireless nodes. ![]() We demonstrate the excellent performance of these auxiliary graphs through simulations and show their applications in efficient routing. We present a distributed local algorithm that, given a quasi-UDG, constructs a nearly planar backbone with a constant stretch factor and a bounded degree. We also study the problem of constructing an energy-efficient backbone for a quasi-UDG. We prove that every quasi-UDG has a corresponding grid graph with small balanced separators that captures its connectivity properties. Network separability is a fundamental property leading to efficient network algorithms and fast parallel computation. In this paper, we present results on two important properties of quasi-UDGs: separability and the existence of power efficient spanners. However, the understanding of the properties of general quasi-UDGs has been very limited, which is impeding the designs of key network protocols and algorithms. A more general network model, the quasi unit-disk graph (quasi-UDG) model, captures much better the characteristics of wireless networks. The significant deviation of the UDG model from many real wireless networks is substantially limiting the applicability of such protocols. Many protocols for wireless networks-routing, topology control, information storage/retrieval and numerous other applications-have been based on the idealized unit-disk graph (UDG) network model. classification ( dataset = 'digits', model_type = 'ensemble', model_name = 'RandomForestClassifier', pickle_name = 'digits_rf.pickle', plot_name = 'digits_rf.A deep understanding of the structural properties of wireless networks is critical for evaluating the performance of network protocols and improving their designs. Or run a whole pipeline with one function: import skippy as skp skp. confusion_matrix_plot ( cm = confmat, acc = accuracy, filename = 'digits_rf.png' ) predict ( x_test ) confmat = confusion_matrix ( y_true = y_test, y_pred = predictions ) accuracy = accuracy_score ( y_true = y_test, y_pred = predictions ) skp. pickle_model ( filename = 'digits_rf.pickle', model = fit ) predictions = fit. get_model ( model_type = 'ensemble', model_name = 'RandomForestClassifier' ) fit = model. get_data ( 'digits' ) x_train, x_test, y_train, y_test = skp. Simplify code to a single function call per step: from trics import confusion_matrix, accuracy_score import skippy as skp data = skp. While the name is the what is actually imported, e.g. Referring to the model type and name from scikit-learn. The following built-in scikit-learn datasets: Will run a linear regression with lasso regularization (L1) Skippy -d diabetes -t linear_model -n Lasso You can also pass arguments to skippy at the command line.įor example, skippy -data diabetes -type linear_model -name Lasso This will produce a 10 x 10 confusion matrix ![]() With no arguments to perform a Logistic Regression In a shell environment, you can run skippy Skip the boilerplate of scikit-learn machine learning examples. ![]()
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