Python Machine learning classifier

Simple classifier

1 ImportNumPy as NP2 ImportMatplotlib.pyplot as MP3x =Np.array ([4[3, 1],5[2, 5],6[1, 8],7[6, 4],8[5, 2],9[3, 5],Ten[4, 7], One[4,-1]]) Ay = Np.array ([0, 1, 1, 0, 0, 1, 1, 0]) -  - #x[:,0] is a numpy array of a notation, for a two-dimensional array, take the two-dimensional array of all the data in the first dimension, the second dimension to take the No. 0 data, visually, x[:,0] is to take all rows of the No. 0 data, the #x[:,1] is the 1th data that takes all rows.  -L, r, h = x[:, 0].min ()-1, x[:, 0].max () + 1, 0.005 -B, T, V = x[:, 1].min ()-1, x[:, 1].max () + 1, 0.005 - #Meshgrid () generates a list of two array elements based on the two one-dimensional array parameters passed in +grid_x =Np.meshgrid (Np.arange (L, R, H), Np.arange (b, T, v)) - #do column Stitching + #Np.c_ is to connect two matrices by row, which is to add the two matrices left and right, requiring equal number of rows, similar to the merge in Pandas () Aflat_x = Np.c_[grid_x[0].ravel (), grid_x[1].ravel ()] atflat_y = Np.zeros (len (flat_x), dtype=int) -  -Flat_y[flat_x[:, 0] < flat_x[:, 1]] = 1 -Grid_y =Flat_y.reshape (Grid_x[0].shape) -  -  inMp.figure ('Simple classfication', facecolor='Lightgray') -Mp.title ('Simple classfication', fontsize=20) toMp.xlabel ('x', fontsize=14) +Mp.ylabel ('y', fontsize=14) -Mp.tick_params (labelsize=10) the #draw a grid with color *Mp.pcolormesh (Grid_x[0], grid_x[1], grid_y, cmap='Gray') $Mp.scatter (x[:, 0], x[:, 1], c=y, cmap='BRG', s=60)Panax NotoginsengMp.show ()

Simple Classification

Logical classification

J (k1,k2,b) = Sigma (-ylog (y ')-(1-y) log (1-y '))/M +m regular function (| | k1,k2,b| |) X regular intensity

Interface:

Sklearn.linear_model. Logisticregression (solver= ' liblinear ', c= regular intensity)

1 ImportNumPy as NP2 ImportSklearn.linear_model as LM3 ImportMatplotlib.pyplot as MP4x =Np.array ([5[4, 7],6[3.5, 8],7[3.1, 6.2],8[0.5, 1],9[1, 2],Ten[1.2, 1.9], One[6, 2], A[5.7, 1.5], -[5.4, 2.2]]) -y = Np.array ([0, 0, 0, 1, 1, 1, 2, 2, 2]) the #creating a logical classifier -Model = LM. Logisticregression (solver='Liblinear', -c=1000) - model.fit (x, y) +L, r, h = x[:, 0].min ()-1, x[:, 0].max () + 1, 0.005 -B, T, V = x[:, 1].min ()-1, x[:, 1].max () + 1, 0.005 +grid_x =Np.meshgrid (Np.arange (L, R, h), A Np.arange (b, T, v)) atflat_x = Np.c_[grid_x[0].ravel (), grid_x[1].ravel ()] -Flat_y =model.predict (flat_x) -Grid_y =Flat_y.reshape (Grid_x[0].shape) -Mp.figure ('Logistic Classification', -Facecolor='Lightgray') -Mp.title ('Logistic Classification', fontsize=20) inMp.xlabel ('x', fontsize=14) -Mp.ylabel ('y', fontsize=14) toMp.tick_params (labelsize=10) +Mp.pcolormesh (Grid_x[0], grid_x[1], grid_y, cmap='Gray') -Mp.scatter (x[:, 0], x[:, 1], c=y, cmap='BRG', s=60) theMp.show ()

Logic Classifier

Python Machine learning classifier