koshian2/ex2_builtin.py

## ex2_builtin.py
import numpy as np
from sklearn.linear_model import LogisticRegression

# データ
X_data = np.array([[34.6236596245169,78.0246928153624],[30.286710768226,43.894997524001],[35.8474087699387,72.9021980270836],[60.1825993862097,86.3085520954682],[79.0327360507101,75.3443764369103],[45.0832774766833,56.3163717815305],[61.1066645368476,96.5114258848962],[75.0247455673888,46.5540135411653],[76.0987867022625,87.420569719268],[84.4328199612003,43.533393310721],[95.8615550709357,38.2252780579509],[75.0136583895824,30.6032632342801],[82.3070533739948,76.481963302356],[69.3645887597093,97.718691961886],[39.5383391436722,76.0368108511588],[53.9710521485623,89.207350137502],[69.0701440628302,52.7404697301676],[67.9468554771161,46.6785741067312],[70.6615095549943,92.9271378936483],[76.9787837274749,47.5759636497553],[67.3720275457087,42.8384383202917],[89.6767757507207,65.7993659274523],[50.534788289883,48.855811527642],[34.2120609778678,44.2095285986628],[77.9240914545704,68.9723599933059],[62.2710136700463,69.9544579544758],[80.1901807509566,44.8216289321835],[93.114388797442,38.800670337132],[61.8302060231259,50.2561078924462],[38.7858037967942,64.9956809553957],[61.379289447425,72.8078873131709],[85.4045193941164,57.0519839762712],[52.1079797319398,63.1276237688171],[52.0454047683182,69.4328601204522],[40.2368937354511,71.1677480218487],[54.6351055542481,52.2138858806112],[33.9155001090688,98.8694357422061],[64.1769888749448,80.9080605867081],[74.7892529594154,41.5734152282443],[34.1836400264419,75.2377203360134],[83.9023936624915,56.3080462160532],[51.5477202690618,46.8562902634997],[94.4433677691785,65.5689216055905],[82.3687537571391,40.6182551597061],[51.0477517712886,45.82270145776],[62.2226757612018,52.0609919483667],[77.1930349260136,70.4582000018095],[97.7715992800023,86.7278223300282],[62.0730637966764,96.7688241241398],[91.5649744980744,88.6962925454659],[79.9448179406693,74.1631193504375],[99.2725269292572,60.9990309984498],[90.5467141139985,43.3906018065002],[34.5245138532,60.3963424583717],[50.2864961189907,49.8045388132305],[49.5866772163203,59.8089509945326],[97.6456339600776,68.861572724206],[32.577200168093,95.5985476138787],[74.2486913672159,69.8245712265719],[71.7964620586337,78.4535622451505],[75.3956114656803,85.7599366733161],[35.2861128152619,47.0205139472341],[56.2538174971162,39.2614725105801],[30.0588224466979,49.5929738672368],[44.6682617248089,66.4500861455891],[66.5608944724295,41.0920980793697],[40.4575509837516,97.5351854890993],[49.0725632190884,51.8832118207396],[80.2795740146699,92.1160608134408],[66.7467185694403,60.9913940274098],[32.7228330406032,43.3071730643006],[64.0393204150601,78.0316880201823],[72.3464942257992,96.227592967614],[60.4578857391895,73.0949980975803],[58.840956217268,75.8584483127904],[99.8278577969212,72.3692519338388],[47.2642691084817,88.4758649955978],[50.4581598028598,75.8098595298245],[60.4555562927153,42.5084094357221],[82.2266615778556,42.7198785371645],[88.9138964166533,69.8037888983547],[94.8345067243019,45.6943068025075],[67.3192574691752,66.5893531774791],[57.2387063156986,59.5142819801295],[80.3667560017127,90.9601478974695],[68.4685217859111,85.5943071045201],[42.0754545384731,78.8447860014804],[75.477702005339,90.4245389975396],[78.6354243489801,96.6474271688564],[52.348003987941,60.7695052560259],[94.0943311251679,77.1591050907389],[90.4485509709636,87.508791764847],[55.4821611406958,35.5707034722886],[74.4926924184304,84.8451368493013],[89.8458067072097,45.3582836109165],[83.4891627449823,48.3802857972817],[42.2617008099817,87.1038509402545],[99.3150088051039,68.7754094720661],[55.340017560037,64.9319380069486],[74.7758930009276,89.5298128951327]])
y = np.array([0,0,0,1,1,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,0,1,0,0,1,1,1,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,1,1,1,1,1,1,0,0,0,1,0,1,1,1,0,0,0,0,0,1,0,1,1,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1])

# 組み込みロジスティック回帰
regr = LogisticRegression(max_iter=400, C=1e10, solver="newton-cg")#デフォルトのliblinearだと係数の値が変わるらしい（精度は同じ） newton-cg
regr.fit(X_data, y)
print("Intercept : ", regr.intercept_)
print("Coef : ", regr.coef_)
print("Expected theta (approx):")
print(" -25.161\n 0.206\n 0.201\n")

# 具体的な値で予測
prob = regr.predict_proba(np.array([45, 85]).reshape(1, -1)) # predict_probaで確率の推定になる、predictは0か1の推定
print("For a student with scores 45 and 85, we predict an admission probability of ")
print("[Not admitted, Admitted] = ", prob) #1つ目の値はy=0である確率（要は不合格の確率）
print("Expected value of admitted: 0.775 +/- 0.002\n")

# 精度
print("Train Accuracy:")
print(regr.score(X_data, y)*100)
print("Expected accuracy (approx): 89.0")
	import numpy as np
	from sklearn.linear_model import LogisticRegression

	# データ
	X_data = np.array([[34.6236596245169,78.0246928153624],[30.286710768226,43.894997524001],[35.8474087699387,72.9021980270836],[60.1825993862097,86.3085520954682],[79.0327360507101,75.3443764369103],[45.0832774766833,56.3163717815305],[61.1066645368476,96.5114258848962],[75.0247455673888,46.5540135411653],[76.0987867022625,87.420569719268],[84.4328199612003,43.533393310721],[95.8615550709357,38.2252780579509],[75.0136583895824,30.6032632342801],[82.3070533739948,76.481963302356],[69.3645887597093,97.718691961886],[39.5383391436722,76.0368108511588],[53.9710521485623,89.207350137502],[69.0701440628302,52.7404697301676],[67.9468554771161,46.6785741067312],[70.6615095549943,92.9271378936483],[76.9787837274749,47.5759636497553],[67.3720275457087,42.8384383202917],[89.6767757507207,65.7993659274523],[50.534788289883,48.855811527642],[34.2120609778678,44.2095285986628],[77.9240914545704,68.9723599933059],[62.2710136700463,69.9544579544758],[80.1901807509566,44.8216289321835],[93.114388797442,38.800670337132],[61.8302060231259,50.2561078924462],[38.7858037967942,64.9956809553957],[61.379289447425,72.8078873131709],[85.4045193941164,57.0519839762712],[52.1079797319398,63.1276237688171],[52.0454047683182,69.4328601204522],[40.2368937354511,71.1677480218487],[54.6351055542481,52.2138858806112],[33.9155001090688,98.8694357422061],[64.1769888749448,80.9080605867081],[74.7892529594154,41.5734152282443],[34.1836400264419,75.2377203360134],[83.9023936624915,56.3080462160532],[51.5477202690618,46.8562902634997],[94.4433677691785,65.5689216055905],[82.3687537571391,40.6182551597061],[51.0477517712886,45.82270145776],[62.2226757612018,52.0609919483667],[77.1930349260136,70.4582000018095],[97.7715992800023,86.7278223300282],[62.0730637966764,96.7688241241398],[91.5649744980744,88.6962925454659],[79.9448179406693,74.1631193504375],[99.2725269292572,60.9990309984498],[90.5467141139985,43.3906018065002],[34.5245138532,60.3963424583717],[50.2864961189907,49.8045388132305],[49.5866772163203,59.8089509945326],[97.6456339600776,68.861572724206],[32.577200168093,95.5985476138787],[74.2486913672159,69.8245712265719],[71.7964620586337,78.4535622451505],[75.3956114656803,85.7599366733161],[35.2861128152619,47.0205139472341],[56.2538174971162,39.2614725105801],[30.0588224466979,49.5929738672368],[44.6682617248089,66.4500861455891],[66.5608944724295,41.0920980793697],[40.4575509837516,97.5351854890993],[49.0725632190884,51.8832118207396],[80.2795740146699,92.1160608134408],[66.7467185694403,60.9913940274098],[32.7228330406032,43.3071730643006],[64.0393204150601,78.0316880201823],[72.3464942257992,96.227592967614],[60.4578857391895,73.0949980975803],[58.840956217268,75.8584483127904],[99.8278577969212,72.3692519338388],[47.2642691084817,88.4758649955978],[50.4581598028598,75.8098595298245],[60.4555562927153,42.5084094357221],[82.2266615778556,42.7198785371645],[88.9138964166533,69.8037888983547],[94.8345067243019,45.6943068025075],[67.3192574691752,66.5893531774791],[57.2387063156986,59.5142819801295],[80.3667560017127,90.9601478974695],[68.4685217859111,85.5943071045201],[42.0754545384731,78.8447860014804],[75.477702005339,90.4245389975396],[78.6354243489801,96.6474271688564],[52.348003987941,60.7695052560259],[94.0943311251679,77.1591050907389],[90.4485509709636,87.508791764847],[55.4821611406958,35.5707034722886],[74.4926924184304,84.8451368493013],[89.8458067072097,45.3582836109165],[83.4891627449823,48.3802857972817],[42.2617008099817,87.1038509402545],[99.3150088051039,68.7754094720661],[55.340017560037,64.9319380069486],[74.7758930009276,89.5298128951327]])
	y = np.array([0,0,0,1,1,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,0,1,0,0,1,1,1,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,1,1,1,1,1,1,0,0,0,1,0,1,1,1,0,0,0,0,0,1,0,1,1,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1])

	# 組み込みロジスティック回帰
	regr = LogisticRegression(max_iter=400, C=1e10, solver="newton-cg")#デフォルトのliblinearだと係数の値が変わるらしい（精度は同じ） newton-cg
	regr.fit(X_data, y)
	print("Intercept : ", regr.intercept_)
	print("Coef : ", regr.coef_)
	print("Expected theta (approx):")
	print(" -25.161\n 0.206\n 0.201\n")

	# 具体的な値で予測
	prob = regr.predict_proba(np.array([45, 85]).reshape(1, -1)) # predict_probaで確率の推定になる、predictは0か1の推定
	print("For a student with scores 45 and 85, we predict an admission probability of ")
	print("[Not admitted, Admitted] = ", prob) #1つ目の値はy=0である確率（要は不合格の確率）
	print("Expected value of admitted: 0.775 +/- 0.002\n")

	# 精度
	print("Train Accuracy:")
	print(regr.score(X_data, y)*100)
	print("Expected accuracy (approx): 89.0")