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We can't make this file beautiful and searchable because it's too large.
ID,Client_Income,Car_Owned,Bike_Owned,Active_Loan,House_Own,Child_Count,Credit_Amount,Loan_Annuity,Accompany_Client,Client_Income_Type,Client_Education,Client_Marital_Status,Client_Gender,Loan_Contract_Type,Client_Housing_Type,Population_Region_Relative,Age_Days,Employed_Days,Registration_Days,ID_Days,Own_House_Age,Mobile_Tag,Homephone_Tag,Workphone_Working,Client_Occupation,Client_Family_Members,Cleint_City_Rating,Application_Process_Day,Application_Process_Hour,Client_Permanent_Match_Tag,Client_Contact_Work_Tag,Type_Organization,Score_Source_1,Score_Source_2,Score_Source_3,Social_Circle_Default,Phone_Change,Credit_Bureau
12202227,11250,0,1,1,1,0,112500,4474.8,Relative,Service,,M,Male,CL,Home,0.019101,20063,2523,2318,3318,,1,0,0,,,2,4,16,Yes,Yes,Self-employed,,0.757150809,0.629674251,0.0515,716,2
12279381,13500,1,1,1,1,3,49752,3252.15,Alone,Service,Secondary,M,Female,CL,Home,0.0105,13200,551,6972,3865,6,1,1,0,Laborers,5,3,3,18,No,Yes,Self-employed,,0.342269008,,,181,
12222714,38250,0,1,0,1,0,67500,3375,Alone
We can't make this file beautiful and searchable because it's too large.
ID,Client_Income,Car_Owned,Bike_Owned,Active_Loan,House_Own,Child_Count,Credit_Amount,Loan_Annuity,Accompany_Client,Client_Income_Type,Client_Education,Client_Marital_Status,Client_Gender,Loan_Contract_Type,Client_Housing_Type,Population_Region_Relative,Age_Days,Employed_Days,Registration_Days,ID_Days,Own_House_Age,Mobile_Tag,Homephone_Tag,Workphone_Working,Client_Occupation,Client_Family_Members,Cleint_City_Rating,Application_Process_Day,Application_Process_Hour,Client_Permanent_Match_Tag,Client_Contact_Work_Tag,Type_Organization,Score_Source_1,Score_Source_2,Score_Source_3,Social_Circle_Default,Phone_Change,Credit_Bureau,Default
12142509,6750,0,0,1,0,0,61190.55,3416.85,Alone,Commercial,Secondary,M,Male,CL,Home,0.028663,13957,1062,6123,383,,1,1,0,Sales,2,2,6,17,Yes,Yes,Self-employed,0.56806615,0.47878667,,0.0186,63,,0
12138936,20250,1,0,1,,0,15282,1826.55,Alone,Service,Graduation,M,Male,CL,Home,0.008575,14162,4129,7833,21,0,1,0,1,,2,2,3,10,Yes,Yes,Government,0.563360099,0.215068341,,,,,0
12181264,18000,0,0,1
This file has been truncated, but you can view the full file.
is_boson,lepton_pT,lepton_eta,lepton_phi,missing_energy_magnitude,missing_energy_phi,jet1pt,jet1eta,jet1phi,jet1b-tag,jet2pt,jet2eta,jet2phi,jet2b-tag,jet3pt,jet3eta,jet3phi,jet3b-tag,jet4pt,jet4eta,jet4phi,jet4b-tag,m_jj,m_jjj,m_lv,m_jlv,m_bb,m_wbb,m_wwbb
1,0.907542109489441,0.329147279262543,0.359411865472794,1.49796986579895,-0.313009530305862,1.09553062915802,-0.557524919509888,-1.58822977542877,2.17307615280151,0.812581181526184,-0.213641926646233,1.27101457118988,2.21487212181091,0.499993950128555,-1.26143181324005,0.732156157493591,0,0.398700892925262,-1.13893008232117,-0.000819110195152462,0,0.302219897508621,0.833048164844513,0.985699653625488,0.978098392486572,0.779732167720795,0.992355763912201,0.79834258556366
1,0.79883474111557,1.47063875198364,-1.63597476482391,0.45377317070961,0.425629168748856,1.10487461090088,1.28232228755951,1.38166427612305,0,0.851737201213837,1.54065895080566,-0.819689512252808,2.21487212181091,0.993489921092987,0.356080114841461,-0.208777546882629,2.54822444915771,1.25695
@msetzu
msetzu / lore.py
Last active August 9, 2021 14:11
import json
from numpy import finfo, inf
from models import Rule
def lore_to_glocalx(json_file, info_file, class_name='class'):
"""Load file `json_file` and `info_file` and return the loaded JSON
rules in a list of `Rule` objects preserving the loading order.
Args:
json_file (str): Path to the JSON file.
@msetzu
msetzu / colored_printf.c
Created February 9, 2019 09:03
Colored printf in C
#include <stdio.h>
/* Colori definiti secondo lo standard ANSI/C */
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
import numpy as np
import pickle
with open('adult.cmpr', 'rb') as log:
adult_cmpr = pickle.load(log)
np.nanmean(adult_cmpr['DT']['fidelity_vector'])
np.nanstd(adult_cmpr['DT']['fidelity_vector'])
@msetzu
msetzu / criteria
Last active August 29, 2015 14:25
dddddSDSADsd
package com.isw2015.alteffequattro.filter;
import com.isw2015.alteffequattro.media.AbstractMedia;
import com.isw2015.alteffequattro.media.Media;
/**
* A functional interface to filter media.
*/