changkun

import Foundation
import objc
import AppKit
import sys

NSUserNotification = objc.lookUpClass('NSUserNotification')
NSUserNotificationCenter = objc.lookUpClass('NSUserNotificationCenter')

def notify(title, subtitle, info_text, delay=0, sound=False, userInfo={}):
	notification = NSUserNotification.alloc().init()

changkun

# wordcount
import string
from functools import reduce

str = """\
How much ground would a groundhog hog, \
if a groundhog could hog ground? \
A gfoundhog would hog all the ground he could hog, \
if a groundhog could hog ground. \
"""

changkun

{
	// general editor settings
	"terminal.external.osxExec": "iTerm.app",
	"workbench.colorTheme": "Material Theme",
	"workbench.iconTheme": "material-theme-icons",
	"vsicons.projectDetection.autoReload": true,
	"editor.lineNumbers": "on",
	"editor.fontSize": 12,
	"editor.rulers": [90, 120],
	"editor.minimap.enabled": true,

changkun

# 使用 numpy 手动实现实现
import numpy as np

# 生成随机数据集
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

# 函数说明:
# 1. np.c_[np.array([1,2,3]), np.array([4,5,6])] --> array([[1, 4],[2, 5],[3, 6]])
# 2. np.ones((2, 1)) --> array([[ 1.], [ 1.]])

changkun

# -- coding: utf-8 --
import numpy as np
import matplotlib.pyplot as plt

# 生成随机数据集
np.random.seed(42)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
X_b = np.c_[np.ones((100, 1)), X]
X_new = np.array([[0], [2]])

changkun

#!/usr/bin/env python3

import numpy as np
from numpy import linalg as LA
import matplotlib.pyplot as plt

# 1. calculate mu
# X = np.array([
#   [-1, 1],
#   [ 0, 0],

changkun

from keras.layers.core import Layer  
from keras import initializers, regularizers, constraints  
from keras import backend as K

class Attention(Layer):
    def __init__(self,
                 kernel_regularizer=None, bias_regularizer=None,
                 kernel_constraint=None, bias_constraint=None,
                 use_bias=True, **kwargs):
        """

changkun

from keras.datasets import mnist
from keras.utils import to_categorical
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Activation, BatchNormalization
from keras.models import Model
from keras import backend as K

# 1. load data
def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)

changkun

while true; do curl -o /dev/null -sw "%{http_code}\n" https://labex.io; sleep .5; done 

changkun

package main

import (
	"encoding/json"
	"fmt"
	"runtime"
	"time"
)

type Monitor struct {