Artificial Intelligence / Reverse Engineering / Internet of Things / Full Stack Developer
支持彩云天气，和风天气，需要注册开发者账号之后添加apikey之后才能使用。
彩云天气：https://open.caiyunapp.com/
和风天气：https://dev.heweather.com/
支持信息：
安装方法：
- 下载zip之后解压缩，放入Domoticz/plugins目录下，重启服务端。去网页端添加硬件即可。
- 切换到插件目录下，git clone https://github.com/obaby/baby_weather_plugin 重启服务端。去网页端添加硬件即可。
每次异常断电树莓派就挂了，之前以为是读写问题，这次停电前主动关机，然后，tm依然挂了。用fsck修复，无数的错误，修复完没有效果。于是重装，重装之后主要就是重新弄好domoticz的博联插件，于是就折腾了一番，把插件的问题进行了修复。
插件原始的wiki页面http://www.domoticz.com/wiki/plugins/BroadlinkRM2.html，已经无法访问了。 并且网上随便一搜，关于这个插件的文章都是四年以前的。新的内容基本没有,我不是插件的原始作者，只是进行了修改和完善。 感谢原作者开发的插件。 虽然一直在用这个插件，但是这个插件配置起来却一点都不轻松，插件依赖于broadlink库。 而这个库在树莓派上安装的时候，虽然能安装成功。但是安装 成功之后多数时间会找不到这个库，从而导致插件启动失败。
插件项目地址：
https://github.com/mjg59/python-broadlink 看项目简介现在已经支持Broadlink RM2, RM3 and RM4 series controllers 。
其实要解决库找不到的问题也简单，不要用pip之类的安装。直接将python-broadlink的broadlink目录拷贝到BroadlinkRM2目录下就可以解决这个问题。
插件进行库搜索的时候会搜索当前插件目录下的库，所以没有必要安装之后再去进行拷贝。 我这里集成的是旧版本的python-broadlink库，如果需要可以替换为新版的库。新版本我没有测试，如果测试ok可以提交个补丁进行说明。
Alec Radford has created some great animations comparing optimization algorithms SGD, Momentum, NAG, Adagrad, Adadelta, RMSprop (unfortunately no Adam) on low dimensional problems. Also check out his presentation on RNNs.
“Noisy moons: This is logistic regression on noisy moons dataset from sklearn which shows the smoothing effects of momentum based techniques (which also results in over shooting and correction). The error surface is visualized as an average over the whole dataset empirically, but the trajectories show the dynamics of minibatches on noisy data. The bottom chart is an accuracy plot.”
“Beale’s function: Due to the large initial gradient, velocity based techniques shoot off and bounce around – adagrad almost goes unstable for the same reason. Algos that scale gradients/step sizes like adadelta and RMSProp proceed more like accelerated SGD and handle large gradients with more stability.”
原生的jupyter theme看起来比较蛋疼，尤其是字体和字号。为了修改这个配置可以安装 jupyter theme。
项目链接： https://github.com/dunovank/jupyter-themes 如果不喜欢英文可以参考这个链接：https://www.jianshu.com/p/6de5f6cce06d
上面的样式对应的配置命令：
jt -f fira -fs 11 -cellw 90% -ofs 11 -dfs 11 -T -t solarizedl
除此之外matplotlib 默认不支持中文显示，主要是字体问题，可以通过下面的代码配置来让matplotlib 支持中文
from matplotlib import pyplot as plt
%matplotlib inline
font = {'family' : 'MicroSoft YaHei',
'weight' : 'bold',
'size' : 10}
plt.rc("font", **font)
实际效果，另外还可以使用altair ，altair 默认支持中文显示 https://altair-viz.github.io
The Challenge
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
这个是kaggle上的一个基础项目，目的是探测泰坦尼克号上的人员的生存概率，项目地址：https://www.kaggle.com/c/titanic
网上基于这个项目其实可以找到各种各样的解决方案，我也尝试了不同的做法。但是实际的效果并不是十分好，个人尝试最好的成绩是0.78468，一次是基于深度神经网络，另外一次就是基于当前的随机森林的模型。
另外还可以看到一系列score为1的提交，这些不知道是怎么做到的，真是太tm牛了~~
自从装好tensorflow-gpu 之后其实一直没怎么用，今天跑代码的时候才发现安装的有问题：
测试代码如下：
from sklearn.datasets import load_sample_image
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
if __name__ == '__main__':
# Load sample images
china = load_sample_image("china.jpg")
flower = load_sample_image("flower.jpg")
dataset = np.array([china, flower], dtype=np.float32)
batch_size, height, width, channels = dataset.shape
# Create 2 filters
filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)
filters[:, 3, :, 0] = 1 # vertical line
filters[3, :, :, 1] = 1 # horizontal line
# Create a graph with input X plus a convolutional layer applying the 2 filters
X = tf.placeholder(tf.float32, shape=(None, height, width, channels))
convolution = tf.nn.conv2d(X, filters, strides=[1,2,2,1], padding="SAME")
with tf.Session() as sess:
output = sess.run(convolution, feed_dict={X: dataset})
plt.imshow(output[0, :, :, 1], cmap="gray") # plot 1st image's 2nd feature map
plt.show()