def recon_1d_sure(data,tree,estimated_var=1.0):
"""
Reconstruction of a function in one dimension using SURE wavelet shrinkage.
"""
haar_coefs = haar.haar_transform(data, tree)
new_haar_coefs = np.zeros(haar_coefs.shape)
coef_levels = haar.level_correspondence(tree)
for level in np.unique(coef_levels):
hc = haar_coefs[coef_levels==level]
if len(hc) == 1:
t = 0
else:
x = np.arange(0,6.0,0.1)
estimates = []
for threshold in x:
estimate = sure(hc,threshold,estimated_var)
estimates.append(estimate[0])
t = x[np.argmin(estimates)]
new_haar_coefs[coef_levels==level] = shrink_coefs(hc,t)
return haar.inverse_haar_transform(new_haar_coefs, tree)
def recon_1d_sure(data, tree, estimated_var=1.0):
"""
Reconstruction of a function in one dimension using SURE wavelet shrinkage.
"""
haar_coefs = haar.haar_transform(data, tree)
new_haar_coefs = np.zeros(haar_coefs.shape)
coef_levels = haar.level_correspondence(tree)
for level in np.unique(coef_levels):
hc = haar_coefs[coef_levels == level]
if len(hc) == 1:
t = 0
else:
x = np.arange(0, 6.0, 0.1)
estimates = []
for threshold in x:
estimate = sure(hc, threshold, estimated_var)
estimates.append(estimate[0])
t = x[np.argmin(estimates)]
new_haar_coefs[coef_levels == level] = shrink_coefs(hc, t)
return haar.inverse_haar_transform(new_haar_coefs, tree)
def train(): global PARTITION print "Importing data" dataset = data_wrangler.extract_data() print "done importing data" haar_dataset = [] for i in range(1,9): print "Haar transform of Gesture: " + str(i) for j in range(len(dataset[i])): if (len(dataset[i][j].shape) == 1): continue datapoint = [haar.haar_transform(dataset[i][j][k]) for k in range(3)] datapoint = np.concatenate(datapoint) if (sum(datapoint) != 0): haar_dataset.append([datapoint,i]) random.shuffle(haar_dataset) if REAL_TRAIN: PARTITION = len(haar_dataset) train_data = map(lambda x: x[0], haar_dataset) train_labels = map(lambda x: x[1], haar_dataset) print "Done Transforms! Now Training" gest_classifier = svm.SVC(C=8,kernel='rbf', gamma=0.5) gest_classifier.fit(train_data[:PARTITION],train_labels[:PARTITION]) print "Done Training! Ready!" if not REAL_TRAIN: count_right = 0 print "Starting Validation" for i in range(PARTITION,len(train_labels)): if gest_classifier.predict(train_data[i])[0] == train_labels[i]: count_right += 1 print "Accuracy : " + str((count_right+0.0)/(len(train_labels)- PARTITION)) def predict(x,y,z): datapoint = [haar.haar_transform(var) for var in [x,y,z]] datapoint = np.concatenate(datapoint) return gest_classifier.predict(datapoint)[0] return predict
def predict(x,y,z): datapoint = [haar.haar_transform(var) for var in [x,y,z]] datapoint = np.concatenate(datapoint) return gest_classifier.predict(datapoint)[0]
import pywt, numpy as np from haar import haar_transform, inverse_haar #print pywt.families() data = np.ones((4,4), dtype=np.float64) #print data coeffs = pywt.dwt2(data, 'haar') cA, (cH, cV, cD) = coeffs #print cA #print cH #print cV mode = 'haar' testa = [1,2,3,4,5,6,7,8] coeffs = pywt.wavedec(testa, mode, level=3) print coeffs #coeffs_H = list(coeffs) #coeffs_H[0] = np.zeros(coeffs_H[0].shape) #print pywt.waverec2(coeffs_H, mode) haar_r = haar_transform(np.asarray(testa, dtype='float')) print haar_r print inverse_haar(haar_r)
