def test_pred(md_clf):
unseen = mpimg.imread("unseen_dig.png")
unseen = interpol_im(unseen, plot_new_im=True)
unseen = rescale_pixel(unseen)
result = md_clf.predict(unseen.reshape(1, -1))
print("The prediction: ", result[0])
from pattern_recog_func import interpol_im, pca_X, rescale_pixel, svm_train, pca_svm_pred
################################################
phys_dict = {0: 'Bohr', 1: 'Einstein'}
bohr = 10
flat_bohr = []
ein = 11
flat_ein = []
y = []
# first load the images and flatten them and store the flattened image objects into a list with length=21,
# then concatenate them into a 21x2700 array
for i in range(bohr):
im = mpimg.imread('bohr' + str(i) + '.jpeg')
flat_bohr.append(interpol_im(im, dim1=45, dim2=60))
y.append(0)
for i in range(ein):
im = mpimg.imread('ein' + str(i) + '.jpeg')
flat_ein.append(interpol_im(im, dim1=45, dim2=60))
y.append(1)
# concatenate into a 21x2700 array
X = np.vstack((np.array(flat_bohr), np.array(flat_ein)))
###################################### Training ########################################
### instantiating PCA and CLF
md_pca, X_proj = pca_X(X)
md_clf = svm_train(X_proj, y)
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn import preprocessing
from sklearn import svm
bohr = 10
ein = 11
flat_bohr = []
flat_ein = []
y = []
phys_dict = {0: "Bohr", 1: "Einstein"}
for i in range(bohr):
im = mpimg.imread("bohr" + str(i) + ".jpeg")
flat_bohr.append(interpol_im(im, dim1=45, dim2=60, plot_new_im=False, cmap="binary", axis_off=True))
y.append(0)
for i in range(ein):
im = mpimg.imread("ein" + str(i) + ".jpeg")
flat_ein.append(interpol_im(im, dim1=45, dim2=60, plot_new_im=False, cmap="binary", axis_off=True))
y.append(1)
X = np.vstack((np.array(flat_bohr), np.array(flat_ein)))
md_pca, X_proj = pca_X(X)
perc = 0
tot = 21
for select_idx in range(tot):
# set_trace()
from sklearn import preprocessing
import warnings
warnings.filterwarnings("ignore")
from pattern_recog_func import interpol_im, pca_X, rescale_pixel, svm_train, pca_svm_pred
################################################
dig_data = load_digits()
X = dig_data.data
y = dig_data.target
index = 15
dig_img = dig_data.images
unseen = mpimg.imread('unseen_dig.png')
flatten_unseen = interpol_im(unseen, plot_new_im = False)
rescaled_unseen = rescale_pixel(X, flatten_unseen, index, plot_ref = True, plot_unseen = True)
print('Mean pixel value of the rescaled image and the X[{}] image are: {}, {}, repectively'.format(index, np.mean(rescaled_unseen), np.mean(X[15])))
print('The rescaled unseen has all the pixel values as integers, flatten array shown as following:')
print(rescaled_unseen)
###################### Trianing ##########################
dig_data = load_digits()
X = dig_data.data
y = dig_data.target
md_clf = svm_train(X[0:60], y[0:60], gamma = 0.001, C = 100)
####################### Validation #########################
import warnings
warnings.filterwarnings("ignore")
from pattern_recog_func import interpol_im, pca_X, rescale_pixel, svm_train, pca_svm_pred
################################################
dig_data = load_digits()
X = dig_data.data
y = dig_data.target
index = 15
dig_img = dig_data.images
unseen = mpimg.imread('unseen_dig.png')
flatten_unseen = interpol_im(unseen, plot_new_im=False)
rescaled_unseen = rescale_pixel(X,
flatten_unseen,
index,
plot_ref=True,
plot_unseen=True)
print(
'Mean pixel value of the rescaled image and the X[{}] image are: {}, {}, repectively'
.format(index, np.mean(rescaled_unseen), np.mean(X[15])))
print(
'The rescaled unseen has all the pixel values as integers, flatten array shown as following:'
)
print(rescaled_unseen)
###################### Trianing ##########################
tot_end = 80
for i in range(tot_start, tot_end):
ans = y[i]
pre = md_clf.predict(X[i].reshape(1, -1))[0]
if ans != pre:
mis += 1
plt.imshow(X[i].reshape((8, 8)), cmap='binary')
plt.show()
print("--------> index, actual digit, svm_prediction: {:d}, {:d}, {:d}".format(i, ans, pre))
if ans == pre:
perc += 1.
percentage = (perc/(tot_end-tot_start))*100.
print("Total number of mis-identifications: {:d}".format(mis))
print("Success rate: {:f}".format(percentage))
############################################################################
############################## unseen_dig.png ##############################
############################################################################
unseen = mpimg.imread('unseen_dig.png')
unseen_flat = interpol_im(unseen, plot_new_im=True)
plt.imshow(X[15].reshape((8, 8)), cmap='binary')
plt.show()
unseen_scaled = rescale_pixel(X, unseen_flat, ind=15)
unseen_pre = md_clf.predict(unseen_flat.reshape(1, -1))
unseen_scaled_pre = md_clf.predict(unseen_scaled.reshape(1, -1))
print("Predictions for unscaled and scaled unseen image: {:d}, {:d}".format(unseen_pre[0], unseen_scaled_pre[0]))
# applying md_clf for the next 20 images
for i in range(tot_start, tot_end):
ans = y[i]
pre = md_clf.predict(X[i].reshape(1, -1))[0]
if ans != pre:
mis += 1
plt.imshow(X[i].reshape((8, 8)), cmap='binary')
plt.show()
print(
"--------> index, actual digit, svm_prediction: {:d}, {:d}, {:d}".
format(i, ans, pre))
if ans == pre:
perc += 1.
percentage = (perc / (tot_end - tot_start))
print("Total number of mis-identifications: {:d}".format(mis))
print("Success rate: {:1.2f}".format(percentage))
# Testing the unseen_dig
unseen = mpimg.imread('unseen_dig.png')
unseen_flat = interpol_im(unseen, plot_new_im=True)
# reshaping the image
plt.imshow(X[15].reshape((8, 8)), cmap='binary')
plt.show()
unseen_scaled = rescale_pixel(X, unseen_flat, ind=15)
# predictions for scaled and unscaled images
unseen_pre = md_clf.predict(unseen_flat.reshape(1, -1))
unseen_scaled_pre = md_clf.predict(unseen_scaled.reshape(1, -1))
print("Predictions for unscaled and scaled unseen image: {:d}, {:d}".format(
unseen_pre[0], unseen_scaled_pre[0]))
from pattern_recog_func import interpol_im, pca_X, rescale_pixel, svm_train, pca_svm_pred
################################################
phys_dict = {0: 'Bohr', 1: 'Einstein'}
bohr = 10
flat_bohr = []
ein = 11
flat_ein = []
y = []
# first load the images and flatten them and store the flattened image objects into a list with length=21,
# then concatenate them into a 21x2700 array
for i in range(bohr):
im = mpimg.imread('bohr'+str(i)+'.jpeg')
flat_bohr.append(interpol_im(im, dim1 = 45, dim2 = 60))
y.append(0)
for i in range(ein):
im = mpimg.imread('ein'+str(i)+'.jpeg')
flat_ein.append(interpol_im(im, dim1 = 45, dim2 = 60))
y.append(1)
# concatenate into a 21x2700 array
X = np.vstack((np.array(flat_bohr), np.array(flat_ein)))
###################################### Training ########################################
### instantiating PCA and CLF
md_pca, X_proj = pca_X(X)
md_clf = svm_train(X_proj, y)
