| "\e[1~": beginning-of-line | |
| "\e[4~": end-of-line | |
| "\e[5~": history-search-backward | |
| "\e[6~": history-search-forward | |
| "\e[3~": delete-char | |
| "\e[2~": quoted-insert | |
| "\e[5C": forward-word | |
| "\e[5D": backward-word | |
| "\e\e[C": forward-word | |
| "\e\e[D": backward-word |
| import tensorflow as tf | |
| import pandas as pd | |
| import numpy as np | |
| from tqdm import tqdm | |
| import os | |
| def _int64_feature(value): | |
| return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) |
| # ===== A Temporal-Difference Learning Snapshot ===== | |
| # Patrick M. Pilarski, pilarski@ualberta.ca, Feb. 11, 2017 | |
| # ---- | |
| # 'xt' and 'xpt1' are the state information for the current (time t) and next (time t+1) time steps in the form of binary vectors | |
| # e.g., if you had a house with five rooms, 'xtp1' could be [0,0,1,0,0], with the single '1' indicating that you are in Room 3; | |
| # in this case, 'xt' might be [0,1,0,0,0] indicating you were just in Room 2; | |
| # for a robot servo, instead of "rooms" you could use binned joint angle ranges. | |
| # 'r' is the signal to be predicted; this is a scalar and could represent reward, or any other signal of interest | |
| # e.g., in the examples above, it might be the reward you get for entering a given room, or the current draw of the servo. | |
| # 'gamma' is the desired time scale of the prediction (with 0 <= gamma <= 1); |
| package main | |
| import "fmt" | |
| import "golang.org/x/sys/unix" | |
| import "net" | |
| import "log" | |
| import "encoding/binary" | |
| import "flag" | |
| // CANMsg almacena un mensaje de la red CAN |
Raspberry Pi is costeffect linux computer very commonly used for IoT home automation projects.
Here are the 3 problems with conventional databases for IoT data store applications:
Thanks to @luxas create the Kubernetes on ARM project. But my project has some different, i have a VM on x86 as master node and two nvidia tx2 development kits as work node. So my kubernetes cluster are multi-platform. I use kubeadm as a deployment method.Some basic information of my platform is as follows.
kubeadm version (use kubeadm version):1.10.0
Environment:
kubectl version):1.10.0
| """ | |
| Train a neural network to implement the discrete Fourier transform | |
| """ | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| N = 32 | |
| batch = 10000 |
