`
다른 분의 풀이를 보고 이전 값의 제곱 값에 $_{2^{i+1}-2} \mathrm{C}_{2^i-1}$ 를 곱하는 원리 찾음
해당하는 조합 숫자는 파스칼의 삼각형 원리 이용
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 import java.
`
bfs + backtracking
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 import java.
Bike demand predict 1 2 3 4 5 6 7 8 9 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler train = pd.read_csv("../input/bike-sharing-demand/train.csv") test = pd.read_csv("../input/bike-sharing-demand/test.csv") train.head() 1 test.
1 2 3 4 import pandas as pd import os df = pd.read_csv("car-good.csv") 1 2 3 # 특징과 라벨 분리 X = df.drop('Class', axis = 1) Y = df['Class'] 1 2 3 # 학습 데이터와 평가 데이터 분리 from sklearn.
map
Series만
1 df['winning_rate'] = df['team'].map(lambda x : total_record(x)[3]) apply
복수 개의 컬럼
1 df['winning_rate'] = df.apply(lambda x:relative_record(x['team'], x['against'])[3], axis=1)
1 2 3 4 5 6 7 8 9 10 11 12 13 import os import numpy as np import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve import statsmodels.
1 2 from sklearn import datasets from sklearn.decomposition import PCA ` PCA 함수를 활용하여 PC를 얻어냄. 아래의 경우 PC 2개를 뽑아냄.
1 2 pca=PCA(n_components=2) pca.fit(X) PCA(copy=True, iterated_power='auto', n_components=2, random_state=None, svd_solver='auto', tol=0.0, whiten=False) 아래와 같이 PC score를 얻어냄.