sailist

from sklearn.datasets import make_blobs
from matplotlib import pyplot as plt
import numpy as np

def distance(ix, iy):
    return np.linalg.norm(x_map[ix] - x_map[iy])

def is_cluster(ix):
    return yy[ix] > 0

sailist

// 可视化网站 链接http://athena.ecs.csus.edu/~associationcw/FpGrowth.html
// 对于一些数据可能会出错误

var align = [["A",1,],
["B",2,],
["C",3,],
["D",4,],
["E",5,],
["F",6,],
["G",7,],

sailist

import pandas as pd
import random

A = ["a1","a2","a3","a4"]
B = ["b1","b2","b3","b4"]
C = ["c1","c2","c3","c4"]
D = ["d1","d2"]
ALL = [A,B,C,D]
def sample(size = 6):
    li = []

sailist

ele = $('tbody tr input');
index = 0;

value = [0,0,0,0,0,
0,0,0,0,0,
0,0,0,0,0,
0,0,
1,1,2,0];
txt = "老师教的很好";

sailist

ū#u,1
à#a,4
ǔ#u,3
ǐ#i,3
í#i,2
é#e,2
ó#o,4
ò#o,2
ā#a,1
ú#u,2

sailist

class ParallelModel(keras.models.Model):
    """Subclasses the standard Keras Model and adds multi-GPU support.
    It works by creating a copy of the model on each GPU. Then it slices
    the inputs and sends a slice to each copy of the model, and then
    merges the outputs together and applies the loss on the combined
    outputs.
    """

    def __init__(self, keras_model, gpu_count):
        """Class constructor.

sailist

from keras.layers import Layer
import numpy as np
from keras.layers import Lambda
from librosa.feature import melspectrogram
from keras import backend as K
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import scale as skscale
from scipy import signal

class MelFeature():

sailist

python setup.py sdist bdist_wheel
SET CountI=0
SET BakDir="./dist/"
for /f   %%a in ('dir %BakDir% /a:-d /B /o:-D') do (
rem echo %%a
SET FileName=%%a
SET CountI=CountI+1
rem echo %CountI%
if %CountI% == 0 goto bakup2
 

sailist

Windows Registry Editor Version 5.00
[HKEY_CLASSES_ROOT\.md\ShellNew]
"NullFile"=""
"FileName"="template.md"

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set /p commitmsg=input commitmsg:
git add *
git commit -m %a%
git push