ortsed

WITH RECURSIVE NetworkCTE AS (
    -- Anchor part to select the starting node
    SELECT source_node,
            target_node
    FROM network_connections
    WHERE source_node = 'Paolo' -- Change this to get someone else's network
    
    UNION ALL
    
    -- Recursive part to select connected nodes

ortsed

def bayesian_liklihood(df, target_col, target_val, prior_col):
   
	"""
	 Gets the liklihood of a target value given a prior value using Bayesian formula
	 on a Pandas dataframe
	
	 df: Pandas dataframe
	 target_col: column being predicted
	 target_val: value in the target_col being predicted
	

ortsed

## Python Function ##
from numpy import array, random, arange

def xicor(X, Y, ties=True):
    random.seed(42)
    n = len(X)
    order = array([i[0] for i in sorted(enumerate(X), key=lambda x: x[1])])
    if ties:
        l = array([sum(y >= Y[order]) for y in Y[order]])
        r = l.copy()

ortsed

from scipy.special import loggamma
from scipy.special import expit, logit
import numpy as np

from scipy.optimize import minimize

def logLikelihood(params, y, X):
    b = np.array(params[0:-1])      # the beta parameters of the regression model
    phi = params[-1]                # the phi parameter
    mu = expit(np.dot(X,b))

ortsed

import numpy as np

neg = model.theta_[0].argsort()
print(np.take(X_test.columns, neg[:10]))

print('')

neg = model.sigma_[0].argsort()
print(np.take(X_test.columns, neg[:10]))

ortsed

# Extends USPS api class to calculate time for Standard delivery from zip to zzip

from lxml import etree

def get_time(origin_zip, dest_zip):
    usps = USPSApi(USPS_API_USER, test=True)

    #usps.urls['calc'] = 'PriorityMail&XML={xml}'

    usps.urls['calc'] = 'StandardB&XML={xml}'

ortsed

from typing import Optional, Union
import numpy as np
import pandas as pd

from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.base import BaseEstimator
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from lightgbm import LGBMClassifier, LGBMRegressor

ortsed

#!/usr/bin/env python
import fileinput
import csv
import sys

#https://github.com/jamesmishra/mysqldump-to-csv

# This prevents prematurely closed pipes from raising
# an exception in Python
from signal import signal, SIGPIPE, SIG_DFL

ortsed

import numpy as np


class LPSolution(object):
    def __init__(self):
        self.iterations = None
        self.tolerance = None
        self.intermediates = []
        self.solution = None
        self.solution_string = None

ortsed

def multivariateGaussian(X, mu, sigma):
    k = len(mu)
    sigma=np.diag(sigma)
    X = X - mu.T
    p = 1/((2*np.pi)**(k/2)*(np.linalg.det(sigma)**0.5))* np.exp(-0.5* np.sum(X @ np.linalg.pinv(sigma) * X,axis=1))
    return p
p = multivariateGaussian(X, mu, sigma)