cion p = predict (theta, X) ICT Predict whether the label is 0 or 1 using learned logistic ession parameters theta = PREDICT (theta, X) computes the predictions for X using a hreshold at 0.5 (i.e., if sigmoid (theta'*x) >= 0.5, predict 1) size (X, 1); % Number of training examples a need to return the following variables correctly eros (m, 1); moid (X*theta); =1:m if (p(i) >= 0.5) p(i) =1; else p(i)=0; end

cion p = predict (theta, X) ICT Predict whether the label is 0 or 1 using learned logistic ession parameters theta = PREDICT (theta, X) computes the predictions for X using a hreshold at 0.5 (i.e., if sigmoid (theta'x) >= 0.5, predict 1) size (X, 1); % Number of training examples a need to return the following variables correctly eros (m, 1); moid (Xtheta); =1:m if (p(i) >= 0.5) p(i) =1; else p(i)=0; end

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…
1. The impulse response of a causal system is: h(t) = A cos(wt) e¯¹/¹u(t) where u(t) is the Heaviside step function. The response is measured experimentally with a sampling interval of T. a. Write an expression for the sampled impulse response h[n]. b. Calculate the z transform of h[n] and write an expression for H[z]. Use the tables provided below as necessary. c. Does the system have an infinite impulse response (IIR) or finite impulse response (FIR)? Justify your answer. d. What is the DC gain of H[z]? e. Write a difference equation that describes the output y[n] in terms of input x[n].
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Simulate a probability curve fx(x)~N(µ,δ) in Matlab AND Calculate the cumulative probability fx (x)note: attach matlab code of the simulation, may be in text
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1 Change this code from Matlab to Phython: 응용웅 function plotDecisionBoundary (theta, X, y) %PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with %the decision boundary defined by theta 3 6. PLOTDECISIONBOUNDARY (theta, X, y) plots the data points with + for the positive examples and o for the negative examples. X is assumed to be a either 1) Mx3 matrix, where the first column is an all-ones column for the intercept. 2) MXN, N>3 matrix, wh % Plot Data 9. 10 11 re the first column is all-ones 12 13 plotData (X (:, 2:3), y); 14 hold on 15 if size (X, 2) <= 3 16 % only need 2 points to define a line, so choose two endpoints 17 [min (X (:, 2))-2, max (X (:, 2))+2]; % Calculate the decision boundary line plot_y = (-1./theta (3)). * (theta (2).*plot_x + theta (1)); * Plot, and adjust axes for better viewing plot (plot_x, plot_y) % Legend, specific for the exercise legend ('Admitted', 'Not admitted', 'Decision Boundary') axis ([30, 100, 30, 100]) 18 19 22 23 24 25 else %…
d'T Plot- versus T-T, and using your knowledge of curve fitting find the value of dt the constant k. Write and implement necessary code. dT Hint: Model == kTkT is analogous to y-ax+b dt
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Using R studio programming software, Write a function that takes in two variables, x, and y. The function should compute the correlation coefficient( Pearson's, Spearman, and Kendall). Perform a hypothesis test on the correlation coefficient using the function cor. test()
The standard normal probability density function is a bell-shaped curve that can be represented as 1 f (z) = Use Matlab to generate a plot of this function using function handle plot from z= -5 to 5. Label x-label as frequency and y- axis as z.
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