vcwild

#include <stdio.h>
#include <stdlib.h>

typedef enum {
    BUTTON,
    LABEL,
    PANEL
} UIComponentType;

typedef struct {

vcwild

#include <stdio.h>
#include <stdlib.h>

typedef struct {
    void (*request)(void *self);
} Subject;

typedef struct {
    Subject subject;
} RealSubject;

vcwild

#include <limits.h>
#include <unistd.h>

void    ft_putchar(char c)
{
    write(1, &c, 1);
}

static int    raises_error(long int x, long int y)
{

vcwild

from typing import Any, Generator
from iqe_tasks.constants import HostTypes
import asyncio
import concurrent.futures
from iqe_tasks import TasksApp
from fabric import Connection


class EvokeFirstIterationMeta(type):
    """Force evoking the first iteration of a generator object."""

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from dataclasses import dataclass
from datetime import datetime
from flask import Flask, jsonify
from flask_sqlalchemy import SQLAlchemy


app = Flask(__name__)
db = SQLAlchemy(app)

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import re
import pandas as pd
from google.cloud import bigquery
from google.colab import auth

PROJECT_ID = 'sql-hunt'  # change to your own project

class BqTable:
  
  def __init__(self, table, dataset='samples', active_project='bigquery-public-data', client=None):

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vcwild

# run 00_data_split
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor


selector_model = Lasso(alpha=1.,normalize=True)
selector = SelectFromModel(selector_model, max_features=5, threshold=-np.inf)

selector.fit(Xtrain, ytrain)

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from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.ensemble import RandomForestRegressor

# Feature Selection
k_vs_score=[]
seed = 42

for k in range(2, X_train.shape[1], 2):

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from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

k_vs_score = [] 
initial_features = 2
max_n_features = len(X.columns)
step = 2