wolframalpha

Add-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0
Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0
Start-Service sshd
# OPTIONAL but recommended:
Set-Service -Name sshd -StartupType 'Automatic'
# Confirm the Firewall rule is configured. It should be created automatically by setup.
Get-NetFirewallRule -Name *ssh*
# There should be a firewall rule named "OpenSSH-Server-In-TCP", which should be enabled
# If the firewall does not exist, create one
New-NetFirewallRule -Name sshd -DisplayName 'OpenSSH Server (sshd)' -Enabled True -Direction Inbound -Protocol TCP -Action Allow -LocalPort 22

wolframalpha

import pandas as pd
import numpy as np

def calibration_error(y_true, y_prob, n_bins=5, strategy='uniform', return_expected_caliberation_error=True):
    if strategy == 'quantile':  # Determine bin edges by distribution of data
        quantiles = np.linspace(0, 1, n_bins + 1)
        bins = np.percentile(y_prob, quantiles * 100)
        bins[-1] = bins[-1] + 1e-8
    elif strategy == 'uniform':
        bins = np.linspace(0., 1. + 1e-8, n_bins + 1)

wolframalpha

import numpy as np
import pandas as pd
import statsmodels.api as sm

transformations = {'log': np.log,
                   'sqrt': np.sqrt,
                   'sqr': lambda x: np.power(x, 2),
                   'cube': lambda x: np.power(x, 3),
                   'cubert': lambda x: np.power(x, -3),
                   'original': lambda x: x}

wolframalpha

import pandas as pd
import numpy as np

def closest_node(node, nodes):
    nodes = np.asarray(nodes)
    dist_2 = np.sum((nodes - node)**2, axis=1)
    return np.argmin(dist_2)

wolframalpha

from bs4 import BeautifulSoup
import pandas as pd
soup = BeautifulSoup(open('00001.xml'), 'xml')

objs = soup.find_all('object')
data = []
for obj in objs:
    x = [x.get_text() for x in obj.find_all('x')]
    y = [y.get_text() for y in obj.find_all('y')]
    name = obj.find('name').get_text()

wolframalpha

# please install shapely first by `pip install Shapely`
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon

point = Point(0.5, 0.5)
polygon = Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])
# this return a bool
print(polygon.contains(point))

wolframalpha

import datetime

cf_history = [('CF_B_HST_MONTH_24', 'CF_B_HST_OS_AMOUNT_24', 'CF_B_HST_AC_DPD_24'),
 ('CF_B_HST_MONTH_23', 'CF_B_HST_OS_AMOUNT_23', 'CF_B_HST_AC_DPD_23'),
 ('CF_B_HST_MONTH_22', 'CF_B_HST_OS_AMOUNT_22', 'CF_B_HST_AC_DPD_22'),
 ('CF_B_HST_MONTH_21', 'CF_B_HST_OS_AMOUNT_21', 'CF_B_HST_AC_DPD_21'),
 ('CF_B_HST_MONTH_20', 'CF_B_HST_OS_AMOUNT_20', 'CF_B_HST_AC_DPD_20'),
 ('CF_B_HST_MONTH_19', 'CF_B_HST_OS_AMOUNT_19', 'CF_B_HST_AC_DPD_19'),
 ('CF_B_HST_MONTH_18', 'CF_B_HST_OS_AMOUNT_18', 'CF_B_HST_AC_DPD_18'),
 ('CF_B_HST_MONTH_17', 'CF_B_HST_OS_AMOUNT_17', 'CF_B_HST_AC_DPD_17'),

wolframalpha

import re
def get_buckets(statuses):
    status_bucket = []
    for status in statuses:
        dpd = re.search('(\d+) (or above )?day', status, re.IGNORECASE)
        if dpd:
            dpd = int(dpd.groups()[0])
            if dpd <= 30:
                status_bucket.append('SMA-0')
            elif dpd <= 60:

wolframalpha

# %matplotlib inline 
import numpy as np

import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('Sample/Sample.tif')
edges = cv.Canny(img,100,200)
plt.figure(figsize=(30, 25))
# plt.subplot(121),plt.imshow(img,cmap = 'gray')
# plt.title('Original Image'), plt.xticks([]), plt.yticks([])

wolframalpha

import time
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from joblib import Parallel, delayed
start_time = time.time()

texts = ['anirban das', 'chitvan gupta', 'prasad devi', 'devi prasad'] * 40
names = ['anirban das', 'devi prasad'] * 6000

def match_name(params):