| 일 | 월 | 화 | 수 | 목 | 금 | 토 |
|---|---|---|---|---|---|---|
| 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 |
- KNeighborsClassifier
- web 개발
- 대이터
- CES 2O21 참가
- web 사진
- web 용어
- tensorflow
- classification
- java역사
- C언어
- vscode
- 자료구조
- 재귀함수
- discrete_scatter
- cudnn
- mglearn
- pycharm
- html
- 결합전문기관
- Keras
- 머신러닝
- postorder
- paragraph
- 웹 용어
- CES 2O21 참여
- 데이터전문기관
- inorder
- web
- broscoding
- bccard
- Today
- Total
목록전체 글 (690)
bro's coding
plt.savefig('myplot.jpg',dpi=10000)
import numpy as np import matplotlib.pyplot as plt plt.axis([-10,10,-10,10]) plt.vlines([0,5,-5],-10,10,linestyles=':') # 수직으로 점(0,5,-5)에 -10에서 10까지 그려라 plt.hlines([0,5,-5],-10,10,linestyles=':') # ([그리는 좌표],길이,선 스타일) plt.text(0,0,'hello',fontsize=20,va='top',ha='right') # 글 쓰기 # 화살표 plt.annotate('zero',xy=(0,0),xytext=(5,5),arrowprops=dict(facecolor='black')) # (문자,xy=화살표 방향, xytext=문자,화살표 위치,a..
import numpy as np import matplotlib.pyplot as plt plt.subplot(2,2,1) plt.plot([1,2,3,4]) plt.subplot(2,2,2) plt.scatter(np.random.randn(100),np.random.randn(100)) plt.subplot(2,2,3) plt.bar(range(10),range(10)) plt.subplot(2,2,4) plt.imshow(np.random.randn(64,64)) matplotlib.subplot(그래프 여러개 그리기 1) axes[0,0].plot([1,2,3,4]) import numpy as np import matplotlib.pyplot as plt axes[0,0].plot([1,2,3..
axes[0,0].plot([1,2,3,4]) import numpy as np import matplotlib.pyplot as plt axes[0,0].plot([1,2,3,4]) axes[0,1].scatter(np.random.randn(100),np.random.randn(100)) axes[1,0].bar(range(10),range(10)) axes[1,1].imshow(np.random.randn(64,64)) fig.suptitle('Test',fontsize=20) axes[0,0].set_title('Test1') matplotlib.subplot(그래프 여러개 그리기 2) import numpy as np import matplotlib.pyplot as plt plt.subplot..
import numpy as np import matplotlib.pyplot as plt x=np.arange(-1,1,0.1) y=np.arange(-1,1,0.1) X,Y=np.meshgrid(x,y) Z=np.exp(-(X**2+Y**2)) CS=plt.contourf(X,Y,Z,levels=np.arange(-1,1,0.05),alpha=0.5,cmap='seismic') plt.colorbar() CS=plt.contour(X,Y,Z,levels=np.arange(-1,1,0.05)) plt.clabel(CS,inline=2,fontsize=10) https://broscoding.tistory.com/81 matplotlib.contour(등고선 그리기 1) import numpy as np..
import numpy as np import matplotlib.pyplot as plt # x,y의 구간 설정 x=np.arange(-1,1,0.1) y=np.arange(-1,1,0.1) # x,y에 대한 관계좌표 설정 X,Y=np.meshgrid(x,y) # X,Y에 관한 Z값 설정 Z=np.sin(X*Y) #var = plt.contour(X, Y, Z, 레벨 설정) CS=plt.contour(X,Y,Z,levels=np.arange(-1,1,0.2)) #주석 달아주기 plt.clabel(CS,inline=2,fontsize=10) # 컬러바 생성 plt.colorbar() https://broscoding.tistory.com/82 matplotlib.contourf(등고선 그리기 2) imp..
import matplotlib.pyplot as plt img=plt.imread('제목 없음.png') plt.imshow(img*[2.,1.,1.],vmax=1)
import matplotlib.pyplot as plt img=plt.imread('제목 없음.png') plt.imshow(img.mean(axis=2),cmap='gray',vmin=0,vmax=1) plt.colorbar()
a = np.ones([4,3]) a ''' array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) ''' a+[1,2,3] ''' [2,3,4] [2,3,4] [2,3,4] [2,3,4] ''' https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html Broadcasting — NumPy v1.17 Manual Broadcasting Note See this article for illustrations of broadcasting concepts. The term broadcasting describes how numpy treats arrays with different shapes d..
