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from typing import Tuple, List | |
class MinMaxScaler: | |
def __init__(self, values: Tuple) -> List: | |
self.values = values | |
self.min_val = min(values) | |
self.max_val = max(values) | |
self.max_min_diff = self.max_val - self.min_val | |
def min_max_scale(self, x: Tuple): |
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from typing import Dict, Iterable, List, KeysView, ItemsView, ValuesView | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
import requests | |
class Pokemon: | |
def __init__(self, name: str = None, number: int = None) -> None: | |
if name and number: |
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def median(vector): | |
sorted_vector = sorted(vector) | |
vector_length = len(vector) | |
middle = vector_length // 2 | |
if vector_length % 2 != 0: | |
return sorted_vector[middle] | |
else: | |
return (sorted_vector[middle] + sorted_vector[middle - 1]) / 2 | |
| |
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from functools import partial | |
def pipe(data, *steps): | |
for step in steps: | |
if callable(step): | |
data = step(data) | |
elif hasattr(type(step), "__iter__"): | |
func, *others = step | |
for arg in others: |
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from sklearn.model_selection import KFold, GridSearchCV | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.datasets import load_boston | |
boston = load_boston() | |
X = boston.data | |
y = boston.target | |
# Create kf instance | |
kf = KFold(n_splits=5, shuffle=True, random_state=42) |
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from sklearn.model_selection import KFold, GridSearchCV | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.datasets import load_boston | |
boston = load_boston() | |
X = boston.data | |
y = boston.target | |
# Create kf instance | |
kf = KFold(n_splits=5, shuffle=True, random_state=42) |
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import pandas as pd | |
from sklearn.model_selection import KFold, GridSearchCV | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.datasets import load_boston | |
boston = load_boston() | |
X = boston.data | |
y = boston.target | |
# Create kf instance |
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import numpy as np | |
import pandas as pd | |
from statsmodels.formula.api import ols | |
from scipy.stats import ttest_ind, mannwhitneyu | |
def permut_concat(iterable): | |
"""Concatenate iterables of arrays then randomize.""" | |
return np.random.permutation(np.concatenate(iterable)) | |
# Make two distributions: a, b |
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from this import s | |
# Without long strings | |
def rot13(x): | |
abc = "".join(map(chr, range(65, 91))) | |
a_to_m = x in abc[:13] + abc[:13].lower() | |
n_to_z = x in abc[13:] + abc[13:].lower() | |
return chr(ord(x) + 13 * (a_to_m or -n_to_z)) |
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from pathlib import Path | |
from functools import partial | |
def chaine(data, *funcs, | |
global_assign=False, | |
print_results=False, | |
write_results=False, | |
**kwargs): | |
chaine_options = any([global_assign, print_results, write_results]) |
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