def distance(vector1, vector2, alpha=2, metric='euclidean'):
'''
Helper function that calculates the alpha
:param vector1:a vector
:type vector1:list of doubles
:param vector2:a vector
:type vector2:list of doubles
:param metric: euclidean, mahalanobis, seuclidean, cityblock
:type metric:string
:rtype: norm between vectors A and B
'''
alpha = numpy.float64(1.0 * alpha)
vector1 = numpy.float64(numpy.array(vector1))
vector2 = numpy.float64(numpy.array(vector2))
if metric == 'euclidean':
vector_norm = distances.euclidean(vector1, vector2)
elif metric == 'mahalanobis':
vi = numpy.linalg.inv(
numpy.cov(numpy.concatenate((vector1, vector2)).T))
vector_norm = distances.mahalanobis(vector1, vector2, vi)
elif metric == 'seuclidean':
vector_norm = distances.seuclidean(vector1, vector2)
elif metric == 'cityblock':
vector_norm = distances.cityblock(vector1, vector2)
elif metric == 'hamming':
vector_norm = distances.hamming(vector1, vector2)
else:
print "Unknown metric"
return None
return vector_norm
def match_state_seq(sts_true, sts_pred, K):
""" Matchs the set of states in sts_pred such that it minimizes the hamming
distance between sts_pred and sts_true. We assume here that the states
are labeled 0, ..., K - 1.
sts_true : A numpy array of integers.
sts_pred : A numpy array of integers.
K : Number of states in case sts_true doesn't cover all states.
"""
sts = np.arange(K, dtype='int')
sts_true = sts_true.astype('int')
sts_pred = sts_pred.astype('int')
min_perm = None
min_hd = np.inf
for p in itertools.permutations(sts):
cur_sts = np.array(p)[sts_pred]
hd = distance.hamming(sts_true, cur_sts)
if hd < min_hd:
min_hd = hd
min_perm = p
return np.array(min_perm)
def eval_ind(individual, initial_pop, model, base_biomass, exp_ess, distance):
# Set this as warning
model.solver = 'gurobi'
old_biomass = list(linear_reaction_coefficients(model).keys())[0] # index removed
old_biomass.remove_from_model()
# Make a biomass reaction and optimize for it
biomass = Reaction('BIOMASS')
model.add_reaction(biomass)
index = initial_pop.index
for i in range(len(index)):
if individual[i] == 1:
biomass.add_metabolites({initial_pop.index[i]: -0.1})
biomass.add_metabolites(base_biomass)
biomass.objective_coefficient = 1.
# Generate deletion results --> BOTTLENECK FOR SURE
deletion_results = single_gene_deletion(model, model.genes, processes=1)
# Filter the results to get a boolean result
a = [(str(next(iter(i))), 1) for i in deletion_results[deletion_results['growth'] > 1e-3].index]
b = [(str(next(iter(i))), 0) for i in deletion_results[deletion_results['growth'] <= 1e-3].index]
c = a + b
pred_ess = pd.DataFrame(c, columns=['Genes', 'Predicted_growth'])
compare_df = pd.merge(right=exp_ess, left=pred_ess, on='Genes', how='inner')
# Apply hamming distance
u = np.array([f for f in compare_df.Measured_growth])
v = np.array([x for x in compare_df.Predicted_growth])
if distance == 'hd':
dist = hamming(u, v)
elif distance == 'mcc':
dist = matthews_corrcoef(u, v)
else:
print('Error: Invalid distance metric')
return dist, sum(individual)
def k_means(data, k=2, distance='e'):
centers = np.array(random.sample(list(data), k))
centers_steps = [centers.tolist()]
changed = True
while changed:
prev_centers = np.copy(centers)
data_nr = data.shape[0]
clusters = np.empty((data_nr, k))
for i in range(data_nr):
if distance == 'e':
clusters[i] = np.array([euclidean(data[i] - centers[j]) for j in range(k)])
elif distance == 'm':
clusters[i] = np.array([cityblock(data[i], centers[j]) for j in range(k)])
elif distance == 'h':
clusters[i] = np.array([hamming(data[i], centers[j]) for j in range(k)])
else:
raise ValueError('Unrecognized distance')
clusters = np.argmin(clusters, axis=1)
for i in range(k):
centers[i] = np.mean(data[np.where(clusters == i)], axis=0)
changed = not np.intersect1d(prev_centers, centers).size == centers.size
centers_steps.append(centers.tolist())
return centers, centers_steps
def hamming_z_score(seq1, seq2, p1=None, p2=None):
"""
Return the z score of the hamming distance under the specified 3 assumptions:
1. P(X_i = Y_i) = P(X_j = Y_j). In words, this means (X_i, Y_i) and
(X_j, Y_j) are identically jointly distributed. In other words, all data
points are equally easy (or hard) to learn (this is an empirically false
assumption).
2. X_i and Y_i are conditionally independent (conditioned on i). In other
words, the predictions between any two learned models on the same test
example are independent (obviously false assumption).
3. (X_i = Y_i) and (X_j = Y_j) are independent. Given assumptions 1 and 2,
this amounts to adding the assumption that the ith and jth predictions from
the same classifier are the independent (obviously false assumption).
"""
# Use given p1 and p2
if p1 is not None and p2 is not None:
expected = expected_hamming(p1, p2)
# Use p1 as single p
elif p1 is not None:
expected = expected_hamming(p1)
# Calculate and use p1 and p2
elif p1 is None and p2 is None:
p1 = torch.mean(seq1)
p2 = torch.mean(seq2)
expected = expected_hamming(p1, p2)
else:
raise ValueError('Invalid arguments: p1 is None and p2 is not None')
std = hamming_std(len(seq1), p1, p2)
return (hamming(seq1, seq2) - expected) / std
def similarity_function(x, y):
""" Similarity function for comparing user features.
This actually really should be implemented in taar.similarity_recommender
and then imported here for consistency.
"""
def safe_get(field, row, default_value):
# Safely get a value from the Row. If the value is None, get the
# default value.
return row[field] if row[field] is not None else default_value
# Extract the values for the categorical and continuous features for both
# the x and y samples. Use an empty string as the default value for missing
# categorical fields and 0 for the continuous ones.
x_categorical_features = [safe_get(k, x, "") for k in CATEGORICAL_FEATURES]
y_categorical_features = [safe_get(k, y, "") for k in CATEGORICAL_FEATURES]
x_continuous_features = [
float(safe_get(k, x, 0)) for k in CONTINUOUS_FEATURES
]
y_continuous_features = [
float(safe_get(k, y, 0)) for k in CONTINUOUS_FEATURES
]
# Here a larger distance indicates a poorer match between categorical variables.
j_d = distance.hamming(x_categorical_features, y_categorical_features)
j_c = distance.canberra(x_continuous_features, y_continuous_features)
# Take the product of similarities to attain a univariate similarity score.
# Add a minimal constant to prevent zero values from categorical features.
# Note: since both the distance function return a Numpy type, we need to
# call the |item| function to get the underlying Python type. If we don't
# do that this job will fail when performing KDE due to SPARK-20803 on
# Spark 2.2.0.
return abs((j_c + 0.001) * j_d).item()
def one_simulation(number, opt_k):
print("\nThe result for %dth experiment with optimal k=%d:" %
(number, opt_k))
kMeans_model = KMeans(n_clusters=opt_k).fit(data_X)
kMeans_labels = kMeans_model.labels_
df['cluster'] = kMeans_labels
labels = ["Family", "Genus", "Species"]
df['Family_label'] = df['cluster'].apply(family_label)
df['Genus_label'] = df['cluster'].apply(genus_label)
df['Species_label'] = df['cluster'].apply(species_label)
ham_dist_once = []
for cluster_id in range(0, opt_k):
cluster = df[df['cluster'] == cluster_id]
for label in labels:
cluster_label = label + "_label"
dist = distance.hamming(cluster[label], cluster[cluster_label])
ham_dist_once.append(dist)
print("Hamming score of cluster %d for label %s is %f" %
(cluster_id, label, dist))
avg_dist = np.mean(ham_dist_once)
ham_dist_all.append(avg_dist)
print("The average hamming score for %dth experiment is %f\n\n" %
(number, avg_dist))
def hamming_diff(seq1, seq2, p1=None, p2=None, ps=None):
"""
Return the difference between the hamming distance of the two sequences and
the expected hamming distance between two random vectors.
If p is specified, use it as the common p for both vector. Otherwise,
calculate each vector's respective p and use those.
"""
# Use the list of ps (not identically distributed)
if ps is not None:
if isinstance(ps, torch.FloatTensor):
assert (len(ps) == len(seq1))
expected = expected_hamming_nonid(ps)
else:
raise ValueError('ps is not FloatTensor')
# Use given p1 and p2
elif p1 is not None and p2 is not None:
expected = expected_hamming(p1, p2)
# Use p1 as single p
elif p1 is not None:
expected = expected_hamming(p1)
# Calculate and use p1 and p2
elif p1 is None and p2 is None:
p1 = torch.mean(seq1)
p2 = torch.mean(seq2)
expected = expected_hamming(p1, p2)
else:
raise ValueError('Invalid arguments: p1 is None and p2 is not None')
return hamming(seq1, seq2) - expected
def find_matches(
pred, #features from user selected image
collection_features, #list of features in the collection
images, #list of filenames associated with the features
dist='cosine' #distance metric - only cosine is good
):
'''
Finds matches for the features of the selected image,
according to the distance metric specified.
Distance metrics use the scipy package
'''
pred = pred.flatten()
nimages = len(collection_features)
#vectorize cosine similarity
# sims= inner(pred,collection_features)/norm(pred)/norm(collection_features,axis=1)
sims = []
for i in range(0, nimages):
if dist == 'euclidean':
sims.append(
distance.euclidean(pred.flatten(),
collection_features[i].flatten()))
elif dist == 'hamming':
pred[pred > 0] = 1
sims.append(
distance.hamming(pred.flatten(),
collection_features[i].flatten()))
else: #default to cosine
sims.append(
distance.cosine(pred.flatten(),
collection_features[i].flatten()))
print('max sim = ' + str(max(sims)))
similar_images = pd.DataFrame({'imgfile': images, 'simscore': sims})
return (similar_images)
def example_of_cross_validation_with_detailed_info(raw_data, labels, num_subjects, num_epochs_per_subj):
# no shrinking, set C=1
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
#logit_clf = LogisticRegression()
clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
# doing leave-one-subject-out cross validation
for i in range(num_subjects):
leave_start = i * num_epochs_per_subj
leave_end = (i+1) * num_epochs_per_subj
training_data = raw_data[0:leave_start] + raw_data[leave_end:]
test_data = raw_data[leave_start:leave_end]
training_labels = labels[0:leave_start] + labels[leave_end:]
test_labels = labels[leave_start:leave_end]
clf.fit(list(zip(training_data, training_data)), training_labels)
# joblib can be used for saving and loading models
#joblib.dump(clf, 'model/logistic.pkl')
#clf = joblib.load('model/svm.pkl')
predict = clf.predict(list(zip(test_data, test_data)))
print(predict)
print(clf.decision_function(list(zip(test_data, test_data))))
incorrect_predict = hamming(predict, np.asanyarray(test_labels)) * num_epochs_per_subj
logger.info(
'when leaving subject %d out for testing, the accuracy is %d / %d = %.2f' %
(i, num_epochs_per_subj-incorrect_predict, num_epochs_per_subj,
(num_epochs_per_subj-incorrect_predict) * 1.0 / num_epochs_per_subj)
)
print(clf.score(list(zip(test_data, test_data)), test_labels))
def compute_clients_dist(self, client_data, cache):
client_categorical_feats = [
client_data.get(specified_key)
for specified_key in CATEGORICAL_FEATURES
]
client_continuous_feats = [
client_data.get(specified_key)
for specified_key in CONTINUOUS_FEATURES
]
# Compute the distances between the user and the cached continuous features.
cont_features = distance.cdist(
cache["continuous_features"],
np.array([client_continuous_feats]),
"canberra",
)
# Compute the distances between the user and the cached categorical features.
cat_features = np.array(
[[distance.hamming(x, client_categorical_feats)]
for x in cache["categorical_features"]])
# See the "Note about cdist optimization" in README.md for why we only use cdist once.
# Take the product of similarities to attain a univariate similarity score.
# Note that the addition of 0.001 to the continuous features
# sets a floor value to the distance in continuous similarity
# scores. There is no such floor value set for categorical
# features so this adjustment prioritizes categorical
# similarity over continous similarity
return (cont_features + FLOOR_DISTANCE_ADJUSTMENT) * cat_features
def get_error_hamming_distributions_from_results(results: Sequence[Sequence[Sequence[int]]]) \
-> Sequence[Sequence[float]]:
"""
Get the distribution of the hamming weight of the error vector (number of bits flipped
between output and expected answer) for each possible pair of two n_bit summands using
results output by get_n_bit_adder_results
:param results: a list of results output from a call to get_n_bit_adder_results
:return: the relative frequency of observing each hamming weight, 0 to n_bits+1, for the error
that occurred when adding each pair of two n_bit summands
"""
num_shots = len(results[0])
n_bits = len(results[0][0]) - 1
hamming_wt_distrs = []
# loop over all binary strings of length n_bits
for result, bits in zip(results, all_bitstrings(2 * n_bits)):
# Input nums are written from (MSB .... LSB) = (a_n, ..., a_1, a_0)
num_a = bit_array_to_int(bits[:n_bits])
num_b = bit_array_to_int(bits[n_bits:])
# add the numbers
ans = num_a + num_b
ans_bits = int_to_bit_array(ans, n_bits + 1)
# record the fraction of shots that resulted in an error of the given weight
hamming_wt_distr = [0. for _ in range(len(ans_bits) + 1)]
for shot in result:
# multiply relative hamming distance by the length of the output for the weight
wt = len(ans_bits) * hamming(ans_bits, shot)
hamming_wt_distr[int(wt)] += 1. / num_shots
hamming_wt_distrs.append(hamming_wt_distr)
return hamming_wt_distrs
def hmmg(X):
hashcode=np.sign(X)
hammin = np.zeros((X.shape[0], X.shape[0])) # penalized hamming(Ci,Cj)
for i in range(X.shape[0] - 1):
for j in range(i + 1, X.shape[0]):
hammin[i,j]=hamming(hashcode[i],hashcode[j])
hammin[j,i]=hammin[i,j]
return hammin
def get_class_from_ECOC(testing_predictions, class_codes):
label = []
for i in range(testing_predictions.shape[0]):
hamming_distances = []
for j in range(class_codes.shape[0]):
hamming_distances.append(distance.hamming(testing_predictions[i], class_codes[j]))
label.append(np.array(hamming_distances).argmin())
return np.array(label)
def distance(user1, user2):
try:
user1Ratings = userItemRatingMatrix.transpose()[str(user1)]
user2Ratings = userItemRatingMatrix.transpose()[str(user2)]
distance = hamming(user1Ratings, user2Ratings)
except:
distance = np.NaN
return distance
def computeDistance(user1, user2):
try:
user1Ratings = userItemRatingMatrix.transpose()[user1]
user2Ratings = userItemRatingMatrix.transpose()[user2]
distance = hamming(user1Ratings, user2Ratings)
except:
distance = np.NAN
return distance
def get_score(motifs):
motif_mx = np.vstack(motifs)
profile = get_profile(motif_mx)
consensus = get_consensus(profile)
return np.sum(
distance.hamming(motif, consensus)
for motif in motif_mx
)
def distance(x, y):
d = 0.
for i in xrange(len(x)):
a = np.int32(list(binary(x[i])))
b = np.int32(list(binary(y[i])))
d = d + hamming(a,b)
d = d / len(x)
return d
def hamming_distance(training_set_vectors, query_vector, top_n=50):
distances = []
# comparing each image to all training set
for i in range(len(training_set_vectors)):
distances.append(hamming(training_set_vectors[i], query_vector[0]))
# return sorted indices of 30 most similar images
return np.argsort(distances)[:top_n]
def mapfn(k, v):
import kdt_config # import has to be under function. cf. mincemeat README.
from dkdt_chunk_ends import chunk_ends
from scipy.spatial.distance import hamming
import numpy as np
sims = [( hamming(np.asarray(chunk),np.asarray(v)) , i ) for i,chunk in enumerate(chunk_ends)]
sims = sorted(sims)
yield sims[-1][1],v # yield one pair so each record is indexed at only one leaf tree.
def _init_similar_matrix(self):
for a1 in self.agents:
for a2 in self.agents:
similarity = 1 - hamming(a1.traits, a2.traits)
similar = similarity >= self.similarity_threshold
self.similar_matrix[a1.index, a2.index] = similar
def hamming_distance(v1, v2):
print('v1: ', v1.shape)
print('v2: ', v2.shape)
if (len(v2) < len(v1)):
v2 = np.concatenate((v2, np.zeros(len(v1) - len(v2))), axis=0)
if (len(v2) > len(v1)):
v1 = np.concatenate((v1, np.zeros(len(v2) - len(v1))), axis=0)
return distance.hamming(v1, v2)
def get_ratio(self,line_list, CLASS_list):
# ersetzt die matches mit leer und subtrahiert das gesamt davon --> Anteil der Matches
#SIZE = float(len(line_list))
#replace = line_list.replace(str(CLASS_list), "")
#print(str(CLASS_list).replace("]","").replace("[",""))
ratio = distance.hamming(line_list,CLASS_list)
#float(((SIZE - float(len(replace)) )) / SIZE)
return (1 - ratio)
def _instability_score(predicted_label, test_label, k):
"""Computes the stability score (see Lange) for `predicted_label` and
`test_label` assuming `k` possible labels.
"""
# find optimal permutation of labels between predicted and test
test_label_ = _permute(
test_label, _get_optimal_permutation(predicted_label, test_label, k))
# return hamming distance
return hamming(predicted_label, test_label_)
def jaccard(self, id1, id2):
"""
Approximate Jaccard coefficient using minhash
:param id1: Doc ID (key)
:param id2: Doc ID (key)
:return j: Approximate Jaccard coefficient
"""
j = 1 - hamming(self.signatures[id1], self.signatures[id2])
return j
def get_displacement(image0, image1):
"""
Gets displacement (in pixels I think) difference between 2 images using scikit-image
not as accurate as the opencv version i think.
:param image0: reference image
:param image1: target image
:return:
"""
from skimage.feature import (match_descriptors, ORB, plot_matches)
from skimage.color import rgb2gray
from scipy.spatial.distance import hamming
from scipy import misc
image0_gray = rgb2gray(image0)
image1_gray = rgb2gray(image1)
descriptor_extractor = ORB(n_keypoints=200)
descriptor_extractor.detect_and_extract(image0_gray)
keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors
descriptor_extractor.detect_and_extract(image1_gray)
keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
# Sort the matches based on distance. Least distance
# is better
distances12 = []
for match in matches12:
distance = hamming(descriptors1[match[0]], descriptors2[match[1]])
distances12.append(distance)
indices = np.arange(len(matches12))
indices = [index for (_, index) in sorted(zip(distances12, indices))]
matches12 = matches12[indices]
# collect displacement from the first 10 matches
dx_list = []
dy_list = []
for mat in matches12[:10]:
# Get the matching key points for each of the images
img1_idx = mat[0]
img2_idx = mat[1]
# x - columns
# y - rows
(x1, y1) = keypoints1[img1_idx]
(x2, y2) = keypoints2[img2_idx]
dx_list.append(abs(x1 - x2))
dy_list.append(abs(y1 - y2))
dx_median = np.median(np.asarray(dx_list, dtype=np.double))
dy_median = np.median(np.asarray(dy_list, dtype=np.double))
# plot_matches(image0, image1, descriptors1, descriptors2, matches12[:10])
return dx_median, dy_median
def get_hamming_distance_matrix(spike_nums_dur):
n_cells = spike_nums_dur.shape[0]
hamm_dist_matrix = np.zeros((n_cells, n_cells))
for i in np.arange(n_cells):
for j in np.arange(n_cells):
hamm_dist_matrix[i,
j] = sci_sp_dist.hamming(spike_nums_dur[i, :],
spike_nums_dur[j, :])
return hamm_dist_matrix
def jaccard(self, id1, id2):
"""
Approximate Jaccard coefficient using minhash
:param id1: Doc ID (key)
:param id2: Doc ID (key)
:return j: Approximate Jaccard coefficient
"""
j = 1 - hamming(self.signatures[id1], self.signatures[id2])
return j
def calculateHEM(self):
from scipy.spatial.distance import hamming
N = self.Errors.shape[0]
self.HEM = np.zeros((N, N))
for i, ei in enumerate(self.Errors):
for j, ej in enumerate(self.Errors):
self.HEM[i, j] = hamming(ei, ej)
def vote_hamming_distance(votes1, votes2):
ids = np.array(range(len(votes1)))
both_voted = ids[(votes1 != 0) & (votes2 != 0)]
if len(both_voted) == 0:
return 0
v1 = votes1[both_voted]
v2 = votes2[both_voted]
distance = hamming(v1, v2)
return distance
def compute_accuracy(self, X, y):
"""
Computes accuracy in percent. Uses scipy lib.
:param X: Data batch. d x n
:param y: Labels batch. n vector
:return: Prediction accuracy in percent
"""
return 1 - hamming(y, self.predict(X))
def match_iris(self,iris,irisList):
check = False
img = ''
for i in irisList:
if distance.hamming(iris.ravel(),i.ravel()) == 0:
check = True
img = i.ravel()
break
return (check, img)
def ComputeFeatureDistance(F1, F2, dis='L2'):
res = np.zeros([F1.shape[0], F2.shape[0]])
for i in range(F1.shape[0]):
for j in range(F2.shape[0]):
if (dis == 'L2'):
res[i][j] = (np.linalg.norm(F1[i] - F2[j]))
elif (dis == 'hamming'):
res[i][j] = hamming(F1[i], F2[j])
return res
def distance(self, otherPoint):
max_len = max(len(otherPoint.data), len(self.data))
dist = 0.
for i in range(max_len):
l1, l2 = pad_to_match(self.get_data(i), otherPoint.get_data(i))
l1.sort()
l2.sort()
dist += hamming(l1, l2)/max_len
return dist
def dist_between_matrices(A, B):
A_unweighted = np.zeros(A.shape)
B_unweighted = np.zeros(B.shape)
A_unweighted[A != 0] = 1
B_unweighted[B != 0] = 1
return hamming(A_unweighted.flatten(), B_unweighted.flatten())
def hamming_distance(a, b):
'''
compares distance for binary arrays
returns number of features that are not the same
'''
if max(a) > 1:
a[a > 0] = 1
b[b > 0] = 1
return (distance.hamming(a, b))
def stability_score(predicted_label, test_label, k):
"""Computes the stability score (see Lange) for `predicted_label` and
`test_label` assuming `k` possible labels.
"""
# find optimal permutation of labels between predicted and test
test_label_ = permute(test_label,
get_optimal_permutation(predicted_label,
test_label, k))
# return hamming distance
return hamming(predicted_label, test_label_)
def cam_corr(M, cell, test):
cor = []
for i in test:
#r = pearsonr(M[cell,:],M[i,:])[0]
r = hamming(M[cell, :], M[i, :])
if np.isnan(r):
continue
else:
cor.append(r)
return cor
def compute_similarity(self, arr1, arr2):
if self.simfcn == "cosine":
return self.d_to_sim(cosine(arr1, arr2))
elif self.simfcn == "pearson":
return self.d_to_sim(correlation(arr1, arr2))
elif self.simfcn == "hamming":
return 1 - hamming(arr1, arr2)
elif self.simfcn == "jaccard":
return 1 - jaccard(arr1, arr2)
else:
print "Similiarity Function Not Yet Supported"
exit()
def dist(self, other):
"""Return the hamming distance between self and other
Attempts to have other provide its own numpy array, but able
to produce one if necessary.
"""
try:
other_seq_array = other.get_seq_array()
except AttributeError:
other_seq_array = sp.array(list(other))
return hamming(self.get_seq_array(), other_seq_array)
def __find__(self,videorequest):
"""
:param videorequest: videorequest object
:type: object
:rtype: tuple of 2 numpy.array (frames number, hamming distances)
"""
reqsig = videorequest.get_feature('bindct')
lreq = len(reqsig)
dists = []
for i in range(len(self.sigs)-lreq):
print "frame", i
hdist = ssd.hamming(self.sigs[i:i+lreq],reqsig)
dists.append(hdist)
return self.__local_minima_fancy__(dists,window=lreq),lreq
def pair_distance(genome_sig_list, file_list):
H_dist = [[0 for x in range(genome_sig_list.shape[0])] for x in range(genome_sig_list.shape[0])]
E_dist = [[0 for x in range(genome_sig_list.shape[0])] for x in range(genome_sig_list.shape[0])]
C_dist = [[0 for x in range(genome_sig_list.shape[0])] for x in range(genome_sig_list.shape[0])]
for i in range(0, genome_sig_list.shape[0]):
for j in range(0, genome_sig_list.shape[0]):
H_dist[i][j] = distance.hamming(genome_sig_list[i],genome_sig_list[j])
E_dist[i][j] = distance.euclidean(genome_sig_list[i],genome_sig_list[j])
C_dist[i][j] = distance.cosine(genome_sig_list[i],genome_sig_list[j])
output_distance(H_dist, file_list, "hamming_distance.csv")
output_distance(E_dist, file_list, "euclidean_distance.csv")
output_distance(C_dist, file_list, "cosine_distance.csv")
def rd_dist(new_perm, dist_metric):
#some metric where
# [0, 9, 8, 1, 2, 3, 4, 7, 5, 6, 10, 11]
# is worse than
# [0, 1, 2, 3, 8, 9, 4, 7, 5, 6, 10, 11] or [0, 3, 2, 1, 4, 9, 8, 7, 5, 6, 10, 11]
# beta = 0.5
goal = range(len(new_perm))
# metric = ((beta**2 + 1) * ssd.hamming(goal, new_perm) * ssd.euclidean(goal, new_perm))/((beta**2) * ssd.hamming(goal, new_perm) + ssd.euclidean(goal,new_perm))
# metric = ssd.hamming(goal, new_perm) + ssd.euclidean(goal, new_perm)
if dist_metric == "hamming":
metric = ssd.hamming(goal, new_perm)
elif dist_metric == "euclidean":
metric = ssd.euclidean(goal, new_perm)
return metric
def test_hammming_loss():
true = np.random.binomial(n=1, p=.5, size=10).astype('float32')
predicted = np.round(np.random.random(10))
refscore = hamming(true, predicted)
yt = T.fvector('yt')
yp = T.fvector('yp')
f = theano.function([yt, yp], tmetrics.classification.hamming_loss(yt, yp), allow_input_downcast=True)
score = f(true, predicted)
print 'true'
print true
print 'predicted'
print predicted
print 'refscore {}'.format(refscore)
print 'score {}'.format(score)
assert np.allclose(refscore, score)
def analyze_performance(predicted_labels, actual_labels):
""" Returns the proportion of total labels that are acurately matched
Parameters
----------
predicted_labels : 1d array
Consists of numeric labels
actual_labels: 1d array
Consists of numeric labels
Returns
-------
distance : float
'Distance' here equals #matching entries / length(predicted_labels)
"""
normed_distance = hamming(predicted_labels, actual_labels) #between 0 - 1
return 1 - normed_distance
def test_classification():
fake_raw_data = [create_epoch(i) for i in range(20)]
labels = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
# 4 subjects, 4 epochs per subject
epochs_per_subj = 4
# svm
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
training_data = fake_raw_data[0: 12]
clf = Classifier(svm_clf, epochs_per_subj=epochs_per_subj)
clf.fit(training_data, labels)
expected_confidence = np.array([-1.18234421, 0.97403604, -1.04005679,
0.92403019, -0.95567738, 1.11746593,
-0.83275891, 0.9486868])
recomputed_confidence = clf.decision_function(fake_raw_data[12:])
hamming_distance = hamming(np.sign(expected_confidence),
np.sign(recomputed_confidence))
assert hamming_distance <= 1, \
'decision function of SVM with recomputation ' \
'does not provide correct results'
y_pred = clf.predict(fake_raw_data[12:])
expected_output = [0, 0, 0, 1, 0, 1, 0, 1]
hamming_distance = hamming(y_pred, expected_output) * len(y_pred)
assert hamming_distance <= 1, \
'classification via SVM does not provide correct results'
confidence = clf.decision_function(fake_raw_data[12:])
hamming_distance = hamming(np.sign(expected_confidence),
np.sign(confidence))
assert hamming_distance <= 1, \
'decision function of SVM without recomputation ' \
'does not provide correct results'
# logistic regression
lr_clf = LogisticRegression()
clf = Classifier(lr_clf, epochs_per_subj=epochs_per_subj)
clf.fit(training_data, labels[0:12])
expected_confidence = np.array([-4.49666484, 3.73025553, -4.04181695,
3.73027436, -3.77043872, 4.42613412,
-3.35616616, 3.77716609])
recomputed_confidence = clf.decision_function(fake_raw_data[12:])
hamming_distance = hamming(np.sign(expected_confidence),
np.sign(recomputed_confidence))
assert hamming_distance <= 1, \
'decision function of logistic regression with recomputation ' \
'does not provide correct results'
y_pred = clf.predict(fake_raw_data[12:])
expected_output = [0, 0, 0, 1, 0, 1, 0, 1]
hamming_distance = hamming(y_pred, expected_output) * len(y_pred)
assert hamming_distance <= 1, \
'classification via logistic regression ' \
'does not provide correct results'
confidence = clf.decision_function(fake_raw_data[12:])
hamming_distance = hamming(np.sign(expected_confidence),
np.sign(confidence))
assert hamming_distance <= 1, \
'decision function of logistic regression without precomputation ' \
'does not provide correct results'
def generate_one_hit_codon_table():
"""Creates a lookup table for one-hit codon changes using a numbered index
Returns
-------
OrderedDict
"""
_one_hit_codon = OrderedDict()
for i in range(1, 65):
for j in range(1, 65):
anc = GENETIC_CODE.by_index(i)[0]
der = GENETIC_CODE.by_index(j)[0]
dist = hamming(list(anc), list(der))
if dist <= 1/float(3):
_one_hit_codon[(i,j)] = Codon_change(GENETIC_CODE.by_index(i), GENETIC_CODE.by_index(j))
return _one_hit_codon
def hamming_dist(self, full_var_x, true_sts):
""" This function returns the hamming distance between the full
variational distribution on the states, and the true state
sequence, after matching via the munkres algorithm
full_var_x: variational distribution of state sequence. Generate
it with self.full_local_update().
true_sts: true state sequence
Returns float with hamming distance and best permutation to match
the states.
"""
state_sq = np.argmax(full_var_x, axis=1).astype(int) #these are learned states
best_match = util.munkres_match(true_sts, state_sq, self.K)
return dist.hamming(true_sts, best_match[state_sq]), best_match
def eval(self, heldout):
""" evaluate under two metrics: 1-best error rate and hamming """
mistakes = dd(int)
err, ham = 0, 0
N = float(len(heldout))
for x, y, e, c in heldout:
y_pred = self.predict(x, e, c)
err += 0.0 if (y == y_pred).all() else 1
ham += hamming(y, y_pred) * len(y)
for i, (y1, y2) in enumerate(zip(y, y_pred)):
if y1 != y2:
mistakes[i] += 1
return err / N, ham / N, dict(mistakes)
def getDisplacement(Image0, Image1):
Image0Gray = rgb2gray(Image0)
Image1Gray = rgb2gray(Image1)
descriptor_extractor = ORB(n_keypoints=200)
descriptor_extractor.detect_and_extract(Image0Gray)
keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors
descriptor_extractor.detect_and_extract(Image1Gray)
keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
# Sort the matches based on distance. Least distance
# is better
distances12 = []
for match in matches12:
distance = hamming(descriptors1[match[0]], descriptors2[match[1]])
distances12.append(distance)
indices = np.range(len(matches12))
indices = [index for (_, index) in sorted(zip(distances12, indices))]
matches12 = matches12[indices]
# collect displacement from the first 10 matches
dxList = []
dyList = []
for mat in matches12[:10]:
# Get the matching keypoints for each of the images
img1_idx = mat[0]
img2_idx = mat[1]
# x - columns
# y - rows
(x1, y1) = keypoints1[img1_idx]
(x2, y2) = keypoints2[img2_idx]
dxList.append(abs(x1 - x2))
dyList.append(abs(y1 - y2))
dxMedian = np.median(np.asarray(dxList, dtype=np.double))
dyMedian = np.median(np.asarray(dyList, dtype=np.double))
plot_matches(Image0, Image1, descriptors1, descriptors2, matches12[:10])
return dxMedian, dyMedian
def predict(self, x_test):
prediction = np.zeros( x_test.shape[0] )
for i in range( x_test.shape[0] ):
x = x_test[i]
new_code = np.zeros( self.P )
for p in range(self.P):
new_code[p] = self.forests[p].predict(x)
# predict the class whose codeword has the smallest hamming distance to new_code
from scipy.spatial.distance import hamming
min_dist = float(np.inf)
c_predicted = -1
for c in range(self.C):
dist = hamming(new_code, self.code_matrix[c,:])
if dist < min_dist:
min_dist = dist
c_predicted = c
prediction[i] = c_predicted
return prediction
def detect(self,video,hamming_threshold=21.0/64):
"""
:param video: video object
:type: video
"""
dctcuts = []
dctcuts.append(0)
sigs = video.get_feature('bindct')
s = sigs[0]
#TODO use GPGPU module if possible or numpy.ediff1d
for i,s_ in enumerate (sigs[1:]):
if ssd.hamming(s,s_) > hamming_threshold :
dctcuts.append(i+1)
s= s_
dctcuts.append(len(sigs))
self.cuts = numpy.array(dctcuts)
self.video = video
return self.cuts
def ecoc_test():
svms = loader.load_pickle_file(model_path)
te_data= loader.load_pickle_file(te_data_path)
pred = []
for f in te_data[0]:
min_hamming_dist = 1.
match_label = 0
code = []
for s in svms:
c_pred = s.predict([f])[0]
code.append(1 if c_pred == 1 else 0) # replace -1 with 0
for ind, c in enumerate(ecoc):
cur_hd = hamming(c, code)
if cur_hd < min_hamming_dist:
min_hamming_dist = cur_hd
match_label = ind
pred.append(match_label)
return (pred == te_data[1]).sum() / len(te_data[1])
def example_of_correlating_two_components(raw_data, raw_data2, labels, num_subjects, num_epochs_per_subj):
# aggregate the kernel matrix to save memory
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
num_training_samples=num_epochs_per_subj*(num_subjects-1)
clf.fit(list(zip(raw_data[0:num_training_samples], raw_data2[0:num_training_samples])),
labels[0:num_training_samples])
X = list(zip(raw_data[num_training_samples:], raw_data2[num_training_samples:]))
predict = clf.predict(X)
print(predict)
print(clf.decision_function(X))
test_labels = labels[num_training_samples:]
incorrect_predict = hamming(predict, np.asanyarray(test_labels)) * num_epochs_per_subj
logger.info(
'when aggregating the similarity matrix to save memory, '
'the accuracy is %d / %d = %.2f' %
(num_epochs_per_subj-incorrect_predict, num_epochs_per_subj,
(num_epochs_per_subj-incorrect_predict) * 1.0 / num_epochs_per_subj)
)
# when the kernel matrix is computed in portion, the test data is already in
print(clf.score(X, test_labels))
def ecoc_prediction_single(feature, boosts, ecoc):
'''
:param feature:
:param boosts:
:param ecoc: ecoc for predicting
:return:
'''
min_hamming_dist = 1.
match_label = 0
code = []
for b in boosts:
c_pred = b.predict_single(feature)
code.append(1 if c_pred == 1 else 0) # replace -1 with 0
for ind, c in enumerate(ecoc):
cur_hd = hamming(c, code)
if cur_hd < min_hamming_dist:
min_hamming_dist = cur_hd
match_label = ind
return match_label
def example_of_aggregating_sim_matrix(raw_data, labels, num_subjects, num_epochs_per_subj):
# aggregate the kernel matrix to save memory
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
clf = Classifier(svm_clf, num_processed_voxels=1000, epochs_per_subj=num_epochs_per_subj)
rearranged_data = raw_data[num_epochs_per_subj:] + raw_data[0:num_epochs_per_subj]
rearranged_labels = labels[num_epochs_per_subj:] + labels[0:num_epochs_per_subj]
clf.fit(list(zip(rearranged_data, rearranged_data)), rearranged_labels,
num_training_samples=num_epochs_per_subj*(num_subjects-1))
predict = clf.predict()
print(predict)
print(clf.decision_function())
test_labels = labels[0:num_epochs_per_subj]
incorrect_predict = hamming(predict, np.asanyarray(test_labels)) * num_epochs_per_subj
logger.info(
'when aggregating the similarity matrix to save memory, '
'the accuracy is %d / %d = %.2f' %
(num_epochs_per_subj-incorrect_predict, num_epochs_per_subj,
(num_epochs_per_subj-incorrect_predict) * 1.0 / num_epochs_per_subj)
)
# when the kernel matrix is computed in portion, the test data is already in
print(clf.score(None, test_labels))
def load_data(data_name, dir_to_data):
"""
Reading data (also transforming string and binary data to appropriate shape)
"""
if data_name in ["actg1", "actg2", "actg3"]:
data = np.genfromtxt(op.join(dir_to_data, data_name + ".data.gz"), dtype = 'str')
N = len(data)
data_input = np.zeros((N, N))
for i in range(N):
for j in range(N):
data_input[i][j] = Levenshtein.distance(data[i], data[j])
elif data_name in ["binstr1", "binstr2", "binstr3"]:
data = np.genfromtxt(op.join(dir_to_data, data_name + ".data.gz"), dtype = 'str')
N = len(data)
data_input = np.zeros((N, N))
for i in range(N):
for j in range(N):
data_input[i][j] = hamming(list(data[i]), list(data[j]))
else:
data_input = np.loadtxt(op.join(dir_to_data, data_name + ".data.gz"), ndmin = 2)
labels = np.loadtxt(op.join(dir_to_data, data_name + ".labels.gz"), dtype = 'int')
return (data_input, labels)
def mapfn(k, v):
from dkdt_chunk_ends import chunk_ends
from scipy.spatial import kdtree
kdtree.node = kdtree.KDTree.node
kdtree.leafnode = kdtree.KDTree.leafnode
kdtree.innernode = kdtree.KDTree.innernode
#k is 0, ..., M
#v is serialzied KDtree
import kdt_config # import has to be under function. cf. mincemeat README.
import cPickle
dkdt = cPickle.loads(v)
from scipy.spatial.distance import hamming
import numpy as np
for i , q in enumerate(kdt_config.queries):
sims = [( hamming(np.asarray(chunk),np.asarray(q)) , i ) for i,chunk in enumerate(chunk_ends)]
sims = sorted(sims)
sims = sims[-kdt_config.dkdt_S:]
sims = [str(s[1]) for s in sims]
if (str(k) in sims): # search if current tree is attached to a similar top leaf
# check is ith kdtree is close to query q.
nearestNeighbors = dkdt.query(q)
yield i , nearestNeighbors
def get_ecoc(ecoc_path, num_ecoc, class_num):
if path.isfile(ecoc_path):
print('Loading the ecoc...')
best_ecoc = loader.load_pickle_file(ecoc_path)
else:
print('Creating the ecoc...')
best_ecoc = [0, [], []] # distance, ecoc for training, ecoc for predicting
for i in range(100):
n = int(math.pow(2, num_ecoc))
codes = choice(n, class_num)
ecoc_func_codes = []
for i in range(num_ecoc):
ecoc_func_codes.append([])
c_ecoc = []
for c in codes:
bin_s = '{0:0' + str(num_ecoc) + '10b}'.format(c)
bin_s = [int(ss) for ss in bin_s]
c_ecoc.append(bin_s)
for i in range(num_ecoc):
ecoc_func_codes[i].append(bin_s[i])
c_hamming_dist = 0
has_same_code = False
for j in range(len(c_ecoc)):
for k in range(len(c_ecoc)):
if j != k:
c_hd = hamming(c_ecoc[j], c_ecoc[k])
if c_hd == 0:
has_same_code = True
c_hamming_dist += c_hd
if has_same_code:
continue
if c_hamming_dist > best_ecoc[0]:
best_ecoc[0] = c_hamming_dist
best_ecoc[1] = ecoc_func_codes
best_ecoc[2] = c_ecoc
# serialize the best ecoc
loader.save(ecoc_path, best_ecoc)
return best_ecoc
ids=0
for i in range(1,niter):
data = pickle.load(open(model %(eta,i),'rb'))
Amedi=data['Ubs'][1][:,:rk];
Acodei=data['Ubs'][2][:,:rk];
for j in range(i+1,niter):
data = pickle.load(open(model %(eta,j),'rb'))
Amedj=data['Ubs'][1][:,:rk];
Acodej=data['Ubs'][2][:,:rk];
# Med stats
cc=[1-cosine(Amedi[:,r],Amedj[:,r]) for r in range(rk)]
c['med_cosine']=c['med_cosine']+cc
cmean['med_cosine']=cmean['med_cosine']+[np.mean(cc)]
cc=[1-hamming(Amedi[:,r]>=1e-15,Amedj[:,r]>=1e-15) for r in range(rk)]
c['med_hamming']=c['med_hamming']+cc
cmean['med_hamming']=cmean['med_hamming']+[np.mean(cc)]
t1=[np.argsort(Amedi[:,r])[::-1][:K] for r in range(rk)]
t2=[np.argsort(Amedj[:,r])[::-1][:K] for r in range(rk)]
cc=[len(np.intersect1d(t1[r],t2[r]))/float(len(np.union1d(t1[r],t2[r]))) for r in range(rk)]
c['med_jaccard_topK']=c['med_jaccard_topK']+cc
cmean['med_jaccard_topK']=cmean['med_jaccard_topK']+[np.mean(cc)]
cc=[len(np.intersect1d(t1[r],t2[r]))/float(K) for r in range(rk)]
c['med_hamming_topK']=c['med_hamming_topK']+cc
cmean['med_hamming_topK']=cmean['med_hamming_topK']+[np.mean(cc)]
# code stats
