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import cv2 | |
import time | |
import numpy as np | |
IMG_SIZE = (250, 250) | |
NUM_ITERS = 500 | |
def compare_tobytes_runtimes(img1, name1, img2, name2): |
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import cv2 | |
import time | |
import numpy as np | |
IMG_SIZE = (250, 250) | |
NUM_ITERS = 500 | |
def compare_tobytes_runtimes(img1, name1, img2, name2): |
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<iframe src="https://greatergood.berkeley.edu/quizzes/embed/empathy" width="100%" scrolling="no" id="iFrameResizer0" style="overflow: hidden; border:0;"></iframe> | |
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script> | |
<script type="text/javascript" src="https://greatergood.berkeley.edu/quizzes/embedjs"></script> |
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bg_day 288 512 0.0 0.0 0.28125 0.5 | |
bg_night 288 512 0.28515625 0.0 0.28125 0.5 | |
bird0_0 48 48 0.0 0.9472656 0.046875 0.046875 | |
bird0_1 48 48 0.0546875 0.9472656 0.046875 0.046875 | |
bird0_2 48 48 0.109375 0.9472656 0.046875 0.046875 | |
bird1_0 48 48 0.1640625 0.9472656 0.046875 0.046875 | |
bird1_1 48 48 0.21875 0.6308594 0.046875 0.046875 | |
bird1_2 48 48 0.21875 0.6816406 0.046875 0.046875 | |
bird2_0 48 48 0.21875 0.7324219 0.046875 0.046875 | |
bird2_1 48 48 0.21875 0.7832031 0.046875 0.046875 |
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# Configuration #1 | |
# Block all traffic FROM e1, e2, w1, w2 | |
# Test #1: | |
# - run TCP server on e1 | |
# [python test-tcp-server.py 10.0.0.1 1080] | |
# - run TCP client on e2 (e2 -> e1) - disallowed | |
# [python test-tcp-client.py 10.0.0.1 1080] | |
# - run TCP client on e3 (e3 -> e1) - disallowed | |
# [python test-tcp-client.py 10.0.0.1 1080] | |
# Test #2: |
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LMFunctor functor; | |
functor.m = m; | |
functor.n = n; | |
Eigen::LevenbergMarquardt<LMFunctor, float> lm(functor); | |
lm.minimize(x); | |
std::cout << "Optimization results" << std::endl; | |
std::cout << "\ta: " << x(0) << std::endl; | |
std::cout << "\tb: " << x(1) << std::endl; |
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// Compute the jacobian of the functions | |
int df(const Eigen::VectorXf &x, Eigen::MatrixXf &fjac) const | |
{ | |
// 'x' has dimensions n x 1 | |
// It contains the current estimates for the parameters. | |
// 'fjac' has dimensions m x n | |
// It will contain the jacobian of the errors, calculated numerically in this case. | |
float epsilon; |
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// Compute 'm' errors, one for each data point, for the given parameter values in 'x' | |
int operator()(const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const | |
{ | |
// 'x' has dimensions n x 1 | |
// It contains the current estimates for the parameters. | |
// 'fvec' has dimensions m x 1 | |
// It will contain the error for each data point. | |
float aParam = x(0); |
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struct LMFunctor | |
{ | |
// Compute 'm' errors, one for each data point, for the given parameter values in 'x' | |
int operator()(const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const | |
{ | |
// TODO | |
} | |
// Compute the jacobian of the errors | |
int df(const Eigen::VectorXf &x, Eigen::MatrixXf &fjac) const |
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-10.00 -685.80 | |
-9.50 -647.10 | |
-9.00 -602.00 | |
-8.50 -548.90 | |
-8.00 -524.20 | |
-7.50 -490.10 | |
-7.00 -430.60 | |
-6.50 -412.10 | |
-6.00 -345.20 | |
-5.50 -344.10 |
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