beomjunshin-ben

건설환경공학부
건설환경공학전공
건축공학과
건축공학전공
건축학과
건축학과 건축공학전공
건축학과 건축학전공(5년제)
건축학전공
계측제어공학과
계측제어공학전공

beomjunshin-ben

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE en-export SYSTEM "http://xml.evernote.com/pub/evernote-export2.dtd">
<en-export export-date="20120727T073610Z" application="Evernote" version="Evernote Mac 3.0.5 (209942)">
<note><title>Vim Tips</title><content><![CDATA[<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE en-note SYSTEM "http://xml.evernote.com/pub/enml2.dtd">
<en-note style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;">
yank for copy, delete for cut, put for parse
<div><br/></div>
<div>Move in context, not position</div>
<div>/ search forward</div>

beomjunshin-ben

from sklearn.datasets import fetch_mldata
import numpy as np
import matplotlib.pyplot as plt
 
 
class RBM:
    def __init__(self, num_visible, num_hidden, learning_rate=0.1):
        self.num_hidden = num_hidden
        self.num_visible = num_visible
        self.learning_rate = learning_rate

beomjunshin-ben

df = pd.DataFrame({'a': [5,1,6,2,23], 'b': [7,7,7,7,7]})
"""
Out:
    a  b
0   5  7
1   1  7
2   6  7
3   2  7
4  23  7
"""

beomjunshin-ben

configfile: "config.json"

rule target:
	input: expand("--a {a} --b {b} --c {c}", a=config["a"], b=config["b"], c=config["c"])

rule neural_network:
	output: expand("--a {a} --b {b} --c {c}", a=config["a"], b=config["b"], c=config["c"])
	shell: "python a.py {output}"

beomjunshin-ben

beomjunshin-ben

You compare the mean score of each group using a simple significance test, and you obtain p = 0.01, indicating there
is a statistically significant difference between means.
이 상황에서 다음의 질문에 대한 T/F 는 각각 무엇일까요?

1. You have absolutely disproved the null hypothesis ("There is no difference between means")
F: 귀무가설이 맞다고 가정했을 때 현재 observation보다 극단적인 값을 얻을 확률이 0.01이라고 알 뿐 절대적인 disprove는 불가능하다. 그냥 결정하기 나름?

2. There is a 1% probability that the null hypothesis is true
F: p-value means that probability of getting a extreme or more than extreme than what was observed given **that null hypothesis is true**

beomjunshin-ben

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from datetime import datetime
import time
import os

# True Model
w = 5.

beomjunshin-ben

beomjunshin-ben