In [11]:
%matplotlib inline
import os
import glob
import hdf5_getters
import pickle
import itertools
import numpy as np
from sklearn.decomposition import PCA
import time
import matplotlib.pyplot as plt
from scipy.spatial.distance import euclidean
from sklearn import datasets
import random
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics.pairwise import euclidean_distances
random.seed(3222)
np.random.seed(3222)
In [2]:
def get_data(basedir, function, upto=10000, ext='.h5'):
data = []
count = 0
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
if count == upto:
return data
if count%100 == 0:
print count,
h5 = hdf5_getters.open_h5_file_read(f)
data.append(function(h5))
h5.close()
count += 1
return data
# save as pickle to save time in loading
segments_pitches_1000 = get_data('../MillionSongSubset/data', hdf5_getters.get_segments_pitches, upto=200)
pickle.dump(segments_pitches_1000, open("../MillionSongSubset/pitches1000", "wb"))
0 100
In [3]:
segments_pitches_1000
Out[3]:
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array([[ 1. , 0.953, 0.597, ..., 0.318, 0.327, 0.631],
[ 0.009, 0.035, 0.355, ..., 0.162, 0.012, 0.02 ],
[ 0.023, 0.198, 0.854, ..., 0.224, 0.021, 0.049],
...,
[ 0.01 , 0.03 , 0.348, ..., 0.057, 0.013, 0.049],
[ 0.043, 0.043, 0.263, ..., 0.129, 0.045, 0.059],
[ 0.014, 0.014, 0.13 , ..., 0.03 , 0.019, 0.013]]),
array([[ 1. , 1. , 1. , ..., 1. , 1. , 1. ],
[ 1. , 0.291, 0.051, ..., 0.04 , 0.044, 0.436],
[ 0.404, 0.057, 0.027, ..., 0.032, 0.088, 1. ],
...,
[ 1. , 0.069, 0.011, ..., 0.008, 0.011, 0.043],
[ 1. , 0.009, 0.029, ..., 0.002, 0.004, 0.021],
[ 1. , 0.021, 0.01 , ..., 0.004, 0.01 , 0.027]]),
array([[ 0.938, 1. , 0.028, ..., 0.065, 0.371, 0.014],
[ 0.808, 1. , 0.041, ..., 0.08 , 0.256, 0.006],
[ 0.842, 1. , 0.153, ..., 0.009, 0.004, 0.102],
...,
[ 0.005, 0.012, 0.015, ..., 0.02 , 0.003, 0.068],
[ 0.001, 0.002, 0.005, ..., 0.018, 0.002, 0.051],
[ 0.002, 0.004, 0.006, ..., 0.031, 0.005, 0.089]]),
array([[ 1. , 0.944, 0.238, ..., 0.134, 0.126, 0.267],
[ 0.025, 0.034, 0.087, ..., 0.061, 0.614, 0.079],
[ 0.012, 0.018, 0.182, ..., 0.038, 0.579, 0.042],
...,
[ 0.064, 0.047, 0.675, ..., 0.083, 0.043, 0.385],
[ 0.035, 0.043, 0.707, ..., 0.108, 0.043, 0.462],
[ 0.05 , 0.058, 0.915, ..., 0.096, 0.143, 0.336]]),
array([[ 1. , 1. , 1. , ..., 1. , 1. , 1. ],
[ 0.028, 0.144, 1. , ..., 0.019, 0.016, 0.049],
[ 0.025, 0.033, 0.593, ..., 0.053, 0.356, 0.036],
...,
[ 1. , 0.25 , 0.375, ..., 0.585, 0.322, 0.241],
[ 0.939, 0.326, 0.358, ..., 0.099, 0.188, 0.31 ],
[ 1. , 0.423, 0.482, ..., 0.415, 0.568, 0.447]]),
array([[ 1. , 0.795, 0.045, ..., 0.433, 0.608, 0.027],
[ 1. , 0.533, 0.113, ..., 0.101, 0.093, 0.555],
[ 1. , 0.749, 0.424, ..., 0.084, 0.131, 0.748],
...,
[ 1. , 0.488, 0.278, ..., 0.055, 0.07 , 0.876],
[ 0.606, 0.339, 0.172, ..., 0.935, 0.787, 0.616],
[ 0.377, 0.402, 0.482, ..., 0.744, 0.588, 0.425]]),
array([[ 0.343, 0.831, 1. , ..., 0.435, 0.105, 0.164],
[ 0.037, 1. , 0.967, ..., 0.134, 0.009, 0.015],
[ 0.155, 0.982, 1. , ..., 0.105, 0.027, 0.043],
...,
[ 0.127, 1. , 0.222, ..., 0.043, 0.034, 0.132],
[ 0.113, 1. , 0.194, ..., 0.061, 0.025, 0.103],
[ 0.1 , 1. , 0.224, ..., 0.068, 0.024, 0.077]]),
array([[ 0.228, 0.206, 0.123, ..., 0.127, 0.136, 1. ],
[ 0.119, 0.062, 0.09 , ..., 0.062, 0.083, 0.509],
[ 0.014, 0.008, 0.029, ..., 0.007, 0.007, 0.261],
...,
[ 0.013, 0.01 , 0.074, ..., 0.011, 0.013, 0.239],
[ 0.038, 0.014, 0.032, ..., 0.029, 0.036, 0.25 ],
[ 0.046, 0.016, 0.111, ..., 0.013, 0.02 , 0.988]]),
array([[ 1. , 0.311, 0.106, ..., 0.141, 0.057, 0.891],
[ 0.402, 1. , 0.358, ..., 0.054, 0.178, 0.195],
[ 1. , 0.309, 0.338, ..., 0.137, 0.174, 0.614],
...,
[ 1. , 0.51 , 0.37 , ..., 0.362, 0.34 , 0.546],
[ 0.826, 0.509, 0.444, ..., 0.585, 0.589, 1. ],
[ 0.951, 1. , 0.696, ..., 0.236, 0.222, 0.778]]),
array([[ 1. , 0.621, 0.599, ..., 0.338, 0.246, 0.223],
[ 1. , 0.107, 0.096, ..., 0.179, 0.154, 0.146],
[ 1. , 0.357, 0.247, ..., 0.464, 0.293, 0.003],
...,
[ 0.529, 0.117, 0.227, ..., 0.145, 0.577, 1. ],
[ 0.97 , 1. , 0.509, ..., 0.402, 0.628, 0.028],
[ 1. , 0.999, 0.925, ..., 0.791, 0.711, 0.641]]),
array([[ 0.074, 0.046, 0.135, ..., 0.037, 0.066, 0.11 ],
[ 0.033, 0.034, 0.075, ..., 0.038, 0.059, 0.088],
[ 0.058, 0.042, 0.15 , ..., 0.051, 0.099, 0.14 ],
...,
[ 0.21 , 0.125, 0.714, ..., 0.061, 0.153, 0.447],
[ 0.104, 0.096, 0.282, ..., 0.116, 0.129, 0.232],
[ 0.889, 1. , 0.935, ..., 0.267, 0.259, 0.597]]),
array([[ 0.366, 0.35 , 0.235, ..., 0.956, 1. , 0.676],
[ 0.205, 0.138, 0.307, ..., 0.304, 0.322, 0.44 ],
[ 0.123, 0.086, 0.136, ..., 0.186, 0.141, 0.14 ],
...,
[ 0.121, 0.184, 0.175, ..., 0.337, 0.008, 0.004],
[ 0.962, 1. , 0.816, ..., 0.906, 0.9 , 0.765],
[ 0.406, 1. , 0.568, ..., 0.159, 0.112, 0.124]]),
array([[ 0.804, 0.594, 0.59 , ..., 0.639, 0.829, 0.364],
[ 0.872, 0.472, 0.627, ..., 0.571, 0.804, 0.699],
[ 0.742, 0.598, 0.523, ..., 0.513, 0.667, 0.722],
...,
[ 1. , 0.765, 0.406, ..., 0.128, 0.107, 0.345],
[ 0.61 , 0.264, 0.35 , ..., 0.207, 0.106, 0.192],
[ 0.276, 0.144, 0.18 , ..., 0.096, 0.106, 0.068]])]
In [4]:
# use first 100 songs to create vocabulary
vocab_size = 100
def build_set(data):
num_of_items = 0
for i in range(len(data)):
num_of_items += data[i].shape[0]
print num_of_items
results = np.ndarray((num_of_items, data[0].shape[1]))
count = 0
for i in range(len(data)):
for j in range(data[i].shape[0]):
results[count] = data[i][j]
count += 1
return results
print "building vocab set"
vocab_pitches = build_set(segments_pitches_1000[:100])
print vocab_pitches.shape
building vocab set
91912
(91912, 12)
In [8]:
from sklearn.utils.extmath import row_norms
In [9]:
def assign_points_to_labels(centroids, points):
# 1 list for each centroid (will contain indices of points)
k = len(centroids)
labels = np.zeros(points.shape[0], dtype=int)
dists = euclidean_distances(points, centroids)
for i in range(points.shape[0]):
# find nearest centroid to this point
best_centroid = 0
best_distance = dists[i, best_centroid]
for c in range(1, k):
distance = dists[i, c]
if distance < best_distance:
best_distance = distance
best_centroid = c
labels[i] = best_centroid
return labels
def update_centroids(centroids, labels, points):
k = len(centroids)
n,d = points.shape[0], points.shape[1]
sum_points = np.zeros([k, d])
n_elements = np.zeros(k, dtype=int)
for i in range(n):
sum_points[labels[i]] += points[i]
n_elements[labels[i]] += 1
for ci in range(k):
if n_elements[ci] != 0:
centroids[ci] = sum_points[ci]/n_elements[ci]
def mykmeans(points, k, max_iter=50, centroids=None):
n_samples, n_features = points.shape
if not centroids:
centroids = [points[x] for x in random.sample(range(n_samples), k)]
labels = None
for p in range(max_iter):
labels = assign_points_to_labels(centroids, points)
update_centroids(centroids, labels, points)
return centroids, labels
def mykmeansplusplus(points, k, max_iter=50):
n_samples, n_features = points.shape
centroidIndices = []
centroidIndices.append(np.random.randint(0, n_samples))
for i in range(k-1):
weightProb = []
leftPointsIndices = [p for p in range(n_samples) if p not in centroidIndices]
dists = euclidean_distances(points[leftPointsIndices], points[centroidIndices])
for p in range(len(leftPointsIndices)):
best_D = dists[p, 0]
best_p = centroidIndices[0]
for c in range(1, len(centroidIndices)):
D = dists[p, c]
if D < best_D:
best_D = D
weightProb.append(best_D)
cum = sum(weightProb)
weightProb = [p/cum for p in weightProb]
centroidIndices.append(np.random.choice(leftPointsIndices, p=weightProb))
centroids = [points[c] for c in centroidIndices]
print centroids
return mykmeans(points, k, max_iter, centroids)
In [36]:
from sklearn.cluster import KMeans
#def learnvocabulary(vocab_data, num_clusters, max_iter=50):
# return mykmeansplusplus(vocab_data, num_clusters, max_iter)
for k in [2,4,8,16,32,64]:
t1 = time.time()
sk_kmeans = KMeans(n_clusters=k, init='random', max_iter=50)
sk_kmeans.fit(vocab_pitches)
print time.time()-t1
1.49621605873
5.83728599548
9.36794686317
28.6680290699
54.6745979786
114.990983009
In [41]:
def getbof(centroids, signal):
bof_tf = np.zeros(len(centroids))
labels = assign_points_to_labels(centroids, signal)
for i in range(labels.shape[0]):
bof_tf[labels[i]] += 1
return bof_tf
# test on one example
bof = getbof(centroids, segments_pitches_1000[199])
In [48]:
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
plt.bar(range(64), bof)
plt.xlabel("Bins for Codewords/Centroids")
plt.ylabel("Number of features in a particular bin")
Out[48]:
<matplotlib.text.Text at 0x7f49b912f690>
In [30]:
def cache_nearest_centroids(centroids, points, k):
points_to_clusters = [-1 for i in range(len(points))]
dists = euclidean_distances(centroids, points)
for i in range(len(points)):
# find nearest centroid to this point
best_centroid = 0
best_distance = dists[best_centroid, i]
for c in range(1, k):
distance = dists[c, i]
if distance < best_distance:
best_distance = distance
best_centroid = c
points_to_clusters[i] = best_centroid
return points_to_clusters
def minibatch_kmeans(X, k, b, max_iter=300, C=None):
n_samples, n_features = X.shape
if not C:
C = [X[c] for c in random.sample(xrange(n_samples), k)]
for i in xrange(max_iter):
v = [0 for i in xrange(k)]
M = [X[j] for j in random.sample(xrange(n_samples), b)]
d = cache_nearest_centroids(C, M, k)
for j in range(len(M)):
c = C[d[j]]
v[d[j]] += 1
eta = 1./v[d[j]]
C[d[j]] = (1-eta)*c + eta*M[j]
return C
In [39]:
#for k in [2,4,8,16,32,64,128,256,512]:
k = 64
t1 = time.time()
centroids = minibatch_kmeans(vocab_pitches, k, 1000)
print time.time() - t1
5.69690585136
In [54]:
def vlad(centroids, signal):
# signal = variable, d
# centroids = k, d
k = len(centroids)
d = signal.shape[1]
v = np.zeros([k, d])
#t = time.time()
labels = assign_points_to_labels(centroids, signal)
#print time.time()-t
# predicted_clusters contains indices of descriptors in signal belonging to different centroids.
for i in range(labels.shape[0]):
v[labels[i]] += signal[i] - centroids[labels[i]]
# Global L2 normalization
n_v = v.reshape(k*d)
n_v = n_v/np.sqrt(n_v.dot(n_v))
return n_v
In [28]:
plt.matshow(segments_pitches_1000[199][:40].reshape(12,40), cmap=plt.cm.Blues)
segments_pitches_1000[199][:40].reshape(12,40)
Out[28]:
array([[ 0.804, 0.594, 0.59 , 0.508, 0.634, 1. , 0.735, 0.478,
0.789, 0.639, 0.829, 0.364, 0.872, 0.472, 0.627, 0.523,
0.537, 1. , 0.513, 0.861, 0.747, 0.571, 0.804, 0.699,
0.742, 0.598, 0.523, 0.754, 0.599, 1. , 0.62 , 0.33 ,
0.351, 0.513, 0.667, 0.722, 1. , 0.246, 0.357, 0.263],
[ 0.369, 0.339, 0.178, 0.218, 0.209, 0.481, 0.25 , 0.389,
1. , 0.19 , 0.24 , 0.161, 0.285, 0.299, 0.134, 0.155,
0.211, 0.376, 0.147, 0.228, 1. , 0.122, 0.125, 0.132,
0.292, 0.255, 0.068, 0.168, 0.163, 0.593, 0.094, 0.199,
1. , 0.064, 0.074, 0.07 , 0.195, 0.223, 0.054, 0.143],
[ 0.153, 0.408, 0.085, 0.21 , 0.93 , 0.09 , 0.133, 0.129,
0.392, 0.557, 0.073, 0.195, 0.167, 1. , 0.133, 0.257,
1. , 0.316, 0.247, 0.286, 0.43 , 0.686, 0.205, 0.331,
0.231, 0.786, 0.446, 0.769, 0.515, 0.796, 1. , 0.389,
0.326, 0.541, 0.997, 0.93 , 0.761, 0.904, 0.486, 0.272],
[ 0.649, 0.68 , 0.585, 0.402, 0.582, 1. , 0.988, 0.561,
0.635, 0.636, 0.482, 0.297, 0.263, 0.533, 0.756, 0.202,
0.412, 1. , 0.113, 0.135, 0.157, 0.288, 0.435, 0.229,
0.375, 0.533, 1. , 0.245, 0.223, 0.172, 0.173, 0.072,
0.112, 0.305, 0.129, 0.258, 0.149, 0.177, 1. , 0.067],
[ 0.047, 0.076, 0.215, 0.014, 0.035, 0.236, 0.026, 0.095,
1. , 0.168, 0.147, 0.13 , 0.147, 0.157, 0.128, 0.134,
0.087, 0.181, 0.374, 0.343, 0.356, 1. , 0.211, 0.096,
0.271, 0.905, 0.09 , 0.188, 0.67 , 0.142, 0.094, 0.323,
0.396, 0.135, 0.566, 0.159, 0.133, 1. , 0.285, 0.057],
[ 0.373, 0.116, 0.666, 0.255, 1. , 0.328, 0.148, 0.552,
0.466, 0.136, 0.29 , 0.64 , 0.213, 0.095, 0.133, 0.27 ,
1. , 0.617, 0.131, 0.095, 0.214, 0.116, 0.104, 0.219,
0.155, 0.372, 0.607, 0.345, 0.151, 1. , 0.178, 0.136,
0.085, 0.67 , 0.064, 0.045, 0.383, 0.137, 0.042, 0.166],
[ 0.499, 1. , 0.3 , 0.385, 0.187, 0.255, 0.116, 0.049,
0.108, 0.086, 0.172, 0.313, 0.481, 0.151, 0.168, 0.144,
0.099, 0.448, 0.371, 0.122, 0.14 , 0.346, 1. , 0.879,
1. , 0.753, 0.08 , 0.133, 0.397, 0.09 , 0.088, 0.447,
0.251, 0.063, 0.503, 0.14 , 0.47 , 0.177, 0.426, 0.103],
[ 0.108, 0.554, 0.309, 0.133, 0.529, 0.143, 1. , 0.593,
0.535, 0.429, 0.635, 0.396, 0.424, 0.578, 0.491, 0.524,
0.691, 0.548, 0.685, 1. , 0.112, 0.642, 0.375, 0.041,
0.053, 1. , 0.19 , 0.063, 0.407, 0.371, 0.061, 0.127,
0.569, 0.108, 0.135, 0.293, 0.199, 0.202, 0.905, 0.252],
[ 0.068, 0.262, 0.264, 1. , 0.589, 0.607, 1. , 0.646,
0.632, 0.566, 0.467, 0.394, 0.443, 0.411, 0.461, 0.465,
0.145, 0.152, 0.097, 0.09 , 0.087, 0.745, 0.878, 0.102,
0.127, 0.175, 0.828, 1. , 1. , 0.269, 0.207, 0.233,
0.702, 0.91 , 0.703, 0.818, 0.257, 0.213, 0.45 , 0.839],
[ 0.262, 0.098, 0.114, 0.123, 1. , 0.326, 0.124, 0.125,
0.064, 0.101, 0.12 , 0.868, 0.985, 0.627, 0.469, 0.189,
0.408, 1. , 0.327, 0.234, 0.386, 0.699, 0.469, 0.822,
0.534, 0.375, 0.281, 0.335, 0.906, 0.364, 0.295, 0.314,
0.503, 0.466, 0.476, 1. , 0.991, 0.731, 0.476, 0.506],
[ 0.605, 0.344, 0.461, 1. , 0.791, 0.67 , 0.353, 0.714,
1. , 0.985, 0.625, 0.375, 0.343, 0.36 , 0.419, 0.768,
0.431, 0.424, 0.408, 0.941, 0.195, 0.22 , 0.366, 0.137,
0.19 , 0.228, 0.355, 1. , 0.614, 0.232, 0.225, 0.358,
0.068, 0.079, 0.141, 0.158, 0.152, 0.307, 0.432, 1. ],
[ 0.299, 0.174, 0.079, 0.054, 0.88 , 1. , 0.581, 0.689,
0.932, 0.918, 0.596, 0.874, 0.972, 0.961, 0.611, 0.265,
0.042, 0.17 , 0.139, 0.269, 0.14 , 0.183, 1. , 0.168,
0.074, 0.066, 0.106, 0.24 , 0.041, 0.088, 0.169, 0.152,
0.123, 0.554, 1. , 0.134, 0.052, 0.083, 0.116, 0.147]])
In [15]:
euclidean_distances(segments_pitches_1000[199],centroids).shape
Out[15]:
(957, 64)
In [68]:
segments_pitches_1000[np.array([2,4,5],dtype=int)]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-68-9c31cae7733d> in <module>()
----> 1 segments_pitches_1000[np.array([2,4,5],dtype=int)]
TypeError: only integer arrays with one element can be converted to an index
In [ ]:
In [ ]:
In [58]:
# test vlad on example
#for k in [2,4,8,16,32,64,128,256,512]:
for k in [2,4,8,16,32,64,128,256,512]:
centroids = minibatch_kmeans(vocab_pitches, k, 1000)
X = segments_pitches_1000[100:200]
#v = np.ndarray([100, 64*12])
t1 = time.time()
for i in range(len(X)):
vlad(centroids, X[i])
print time.time() - t1
print v.shape
print ex.shape
0.30064702034
0.345008134842
0.416347980499
0.564369916916
0.906133174896
1.53074002266
2.75323081017
5.21149611473
10.5667860508
(100, 768)
(2159, 12)
In [11]:
# Use PCA to reduce dimensionality
# Use LSHForest for approximate nearest neighbors
In [12]:
X = segments_pitches_1000[100:200]
v = np.ndarray([100, 64*12])
t1 = time.time()
for i in range(len(X)):
v[i] = vlad(centroids, segments_pitches_1000[i])
print time.time() - t1, 'secs'
141.704943895 secs
In [13]:
# reduce dimensionality to 50
pca = PCA(n_components=50)
%time new_v = pca.fit_transform(v)
print new_v.shape
CPU times: user 16.1 ms, sys: 8 ms, total: 24.1 ms
Wall time: 192 ms
(100, 50)
In [36]:
kmeans = MiniBatchKMeans(n_clusters=64)
%time kmeans.fit(vocab_pitches)
CPU times: user 2.71 s, sys: 3.91 ms, total: 2.72 s
Wall time: 2.72 s
Out[36]:
MiniBatchKMeans(batch_size=100, compute_labels=True, init='k-means++',
init_size=None, max_iter=100, max_no_improvement=10, n_clusters=64,
n_init=3, random_state=None, reassignment_ratio=0.01, tol=0.0,
verbose=0)
In [15]:
vocab_pitches.shape
Out[15]:
(91912, 12)
In [32]:
kmeans.cluster_centers_.shape
Out[32]:
(32, 12)
In [19]:
kmeans.labels_
Out[19]:
array([14, 8, 5, ..., 3, 14, 14], dtype=int32)
In [20]:
kmeans.labels_.shape
Out[20]:
(91912,)
In [28]:
kmeans.labels_[50000]
Out[28]:
22
In [44]:
kmeans.predict(segments_pitches_1000[102]);
In [30]:
segments_pitches_1000[100].shape
Out[30]:
(449, 12)
In [34]:
np.set_printoptions(threshold=np.nan)
In [37]:
# test vlad on example
ex = segments_pitches_1000[197]
%time v1 = vlad(centroids, ex)
print v1.shape
%time v2 = vlad2(ex, kmeans)
print v2.shape
0.0352399349213
CPU times: user 41.6 ms, sys: 10 µs, total: 41.6 ms
Wall time: 41.1 ms
(768,)
CPU times: user 16.5 ms, sys: 0 ns, total: 16.5 ms
Wall time: 16.5 ms
(768,)
In [46]:
v1;
In [47]:
v2;
In [53]:
from sklearn.datasets import make_multilabel_classification
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import LabelBinarizer
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA
def plot_hyperplane(clf, min_x, max_x, linestyle, label):
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(min_x - 5, max_x + 5) # make sure the line is long enough
yy = a * xx - (clf.intercept_[0]) / w[1]
plt.plot(xx, yy, linestyle, label=label)
def plot_subfigure(X, Y, subplot, title, transform):
if transform == "pca":
X = PCA(n_components=2).fit_transform(X)
elif transform == "cca":
X = CCA(n_components=2).fit(X, Y).transform(X)
else:
raise ValueError
min_x = np.min(X[:, 0])
max_x = np.max(X[:, 0])
min_y = np.min(X[:, 1])
max_y = np.max(X[:, 1])
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(X, Y)
plt.subplot(2, 2, subplot)
plt.title(title)
zero_class = np.where(Y[:, 0])
one_class = np.where(Y[:, 1])
plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
facecolors='none', linewidths=2, label='Class 1')
plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
facecolors='none', linewidths=2, label='Class 2')
plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
'Boundary\nfor class 1')
plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',
'Boundary\nfor class 2')
plt.xticks(())
plt.yticks(())
plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)
plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)
if subplot == 2:
plt.xlabel('First principal component')
plt.ylabel('Second principal component')
plt.legend(loc="upper left")
plt.figure(figsize=(8, 6))
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=True,
random_state=1)
print X
print Y
plot_subfigure(X, Y, 1, "With unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 2, "With unlabeled samples + PCA", "pca")
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
random_state=1)
plot_subfigure(X, Y, 3, "Without unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 4, "Without unlabeled samples + PCA", "pca")
plt.subplots_adjust(.04, .02, .97, .94, .09, .2)
[[ 5. 3. 2. 2. 3. 1. 3. 6. 3. 2. 4. 3. 3. 5.
1. 2. 1. 2. 2. 3.]
[ 4. 2. 3. 2. 1. 4. 2. 4. 2. 2. 3. 2. 0. 7.
4. 3. 3. 5. 2. 1.]
[ 0. 0. 3. 5. 8. 3. 3. 1. 3. 1. 2. 2. 0. 3.
0. 4. 1. 2. 4. 3.]
[ 4. 0. 1. 2. 3. 6. 2. 5. 1. 5. 2. 3. 0. 1.
2. 2. 0. 3. 4. 4.]
[ 0. 2. 3. 2. 1. 2. 0. 2. 1. 4. 1. 3. 1. 1.
1. 2. 2. 4. 0. 2.]
[ 1. 2. 2. 1. 3. 3. 2. 3. 4. 1. 3. 2. 3. 1.
3. 0. 4. 3. 4. 1.]
[ 0. 1. 0. 4. 1. 0. 0. 3. 0. 7. 2. 9. 0. 1.
2. 7. 5. 4. 0. 8.]
[ 1. 2. 2. 0. 0. 2. 3. 3. 1. 1. 1. 4. 2. 4.
0. 3. 3. 2. 4. 6.]
[ 4. 7. 3. 3. 3. 1. 3. 5. 1. 1. 1. 3. 2. 3.
0. 1. 1. 1. 5. 1.]
[ 0. 0. 1. 3. 7. 7. 1. 2. 3. 6. 3. 5. 0. 3.
1. 3. 2. 4. 7. 4.]
[ 0. 2. 1. 0. 4. 1. 0. 0. 5. 5. 1. 5. 0. 1.
0. 6. 3. 4. 1. 4.]
[ 0. 0. 1. 8. 1. 2. 0. 4. 0. 5. 2. 8. 1. 2.
0. 6. 6. 3. 0. 9.]
[ 0. 1. 2. 4. 1. 6. 5. 5. 0. 5. 1. 8. 0. 2.
4. 3. 2. 3. 4. 4.]
[ 2. 1. 0. 6. 8. 1. 3. 2. 1. 3. 3. 6. 1. 4.
1. 5. 2. 6. 2. 9.]
[ 2. 2. 1. 4. 3. 1. 4. 5. 3. 3. 4. 2. 0. 3.
3. 2. 1. 1. 4. 1.]
[ 1. 1. 2. 5. 2. 0. 0. 3. 5. 2. 5. 0. 1. 2.
4. 6. 3. 1. 2. 3.]
[ 1. 2. 1. 2. 1. 1. 3. 0. 1. 3. 2. 3. 0. 3.
3. 5. 1. 1. 1. 2.]
[ 3. 0. 0. 4. 4. 2. 3. 0. 1. 7. 4. 2. 0. 2.
1. 1. 3. 1. 2. 2.]
[ 5. 2. 2. 3. 6. 2. 1. 4. 1. 1. 3. 3. 3. 2.
2. 1. 2. 3. 4. 3.]
[ 0. 0. 2. 1. 2. 1. 4. 3. 1. 6. 3. 1. 0. 4.
1. 1. 1. 2. 3. 5.]
[ 1. 0. 3. 3. 0. 3. 5. 3. 0. 6. 7. 6. 0. 11.
2. 4. 2. 3. 3. 1.]
[ 0. 1. 0. 1. 4. 3. 0. 1. 3. 3. 3. 8. 0. 3.
1. 7. 0. 5. 2. 6.]
[ 1. 0. 4. 4. 3. 1. 0. 1. 6. 1. 4. 3. 0. 3.
4. 1. 3. 7. 4. 4.]
[ 0. 2. 0. 2. 4. 1. 0. 7. 3. 7. 4. 6. 0. 0.
0. 9. 5. 4. 0. 7.]
[ 0. 0. 1. 2. 2. 2. 2. 1. 0. 9. 2. 6. 1. 4.
0. 8. 3. 2. 0. 6.]
[ 6. 3. 1. 1. 1. 3. 4. 3. 0. 3. 3. 1. 4. 1.
2. 2. 3. 1. 2. 1.]
[ 4. 3. 7. 0. 2. 3. 2. 4. 3. 5. 1. 5. 2. 1.
3. 2. 5. 4. 2. 1.]
[ 1. 0. 0. 1. 2. 4. 0. 6. 1. 7. 1. 6. 1. 2.
0. 5. 3. 4. 1. 4.]
[ 1. 0. 2. 0. 3. 6. 0. 3. 1. 1. 2. 1. 2. 1.
3. 3. 2. 2. 2. 1.]
[ 1. 0. 2. 1. 2. 6. 1. 1. 1. 5. 1. 7. 0. 1.
2. 5. 1. 4. 2. 5.]
[ 2. 0. 1. 3. 1. 0. 1. 3. 1. 6. 2. 7. 0. 3.
5. 5. 2. 5. 2. 5.]
[ 1. 0. 1. 0. 3. 3. 0. 2. 3. 3. 3. 5. 0. 4.
1. 1. 1. 5. 4. 5.]
[ 1. 1. 0. 2. 4. 1. 2. 5. 3. 3. 3. 2. 2. 6.
6. 0. 2. 4. 3. 0.]
[ 0. 2. 2. 1. 2. 3. 0. 2. 0. 5. 1. 2. 0. 0.
0. 7. 5. 4. 0. 8.]
[ 1. 0. 1. 2. 6. 5. 2. 0. 3. 8. 1. 5. 0. 3.
1. 0. 1. 1. 2. 2.]
[ 2. 0. 4. 4. 2. 2. 2. 2. 2. 3. 4. 3. 4. 1.
2. 2. 1. 2. 2. 2.]
[ 4. 0. 1. 0. 3. 6. 3. 8. 1. 3. 4. 10. 0. 7.
4. 1. 2. 3. 3. 1.]
[ 2. 1. 2. 3. 4. 3. 6. 3. 0. 1. 0. 2. 5. 0.
1. 1. 8. 3. 1. 5.]
[ 4. 1. 3. 3. 1. 1. 4. 3. 1. 2. 2. 4. 1. 4.
2. 4. 1. 2. 2. 3.]
[ 1. 1. 3. 2. 0. 2. 5. 3. 0. 3. 1. 4. 0. 7.
2. 1. 0. 1. 2. 6.]
[ 2. 0. 0. 2. 4. 9. 5. 2. 1. 7. 1. 4. 1. 3.
1. 3. 0. 1. 3. 8.]
[ 0. 1. 1. 4. 2. 0. 0. 4. 2. 10. 4. 9. 0. 1.
1. 10. 5. 6. 0. 6.]
[ 3. 0. 3. 1. 0. 3. 1. 2. 1. 3. 3. 1. 3. 1.
5. 2. 0. 2. 0. 4.]
[ 3. 0. 3. 3. 2. 3. 1. 2. 3. 1. 4. 3. 6. 2.
1. 7. 4. 7. 1. 2.]
[ 3. 0. 3. 3. 2. 1. 1. 6. 1. 5. 1. 0. 2. 4.
3. 4. 0. 1. 5. 1.]
[ 3. 1. 3. 3. 4. 5. 4. 0. 0. 8. 2. 4. 0. 9.
1. 3. 0. 5. 4. 3.]
[ 1. 3. 4. 3. 2. 2. 4. 3. 3. 1. 2. 3. 2. 2.
3. 3. 1. 0. 4. 3.]
[ 1. 0. 0. 1. 3. 4. 4. 2. 2. 4. 5. 8. 0. 4.
1. 2. 1. 3. 4. 5.]
[ 1. 2. 1. 2. 2. 5. 3. 1. 2. 1. 5. 4. 3. 4.
3. 2. 0. 3. 5. 3.]
[ 2. 3. 2. 5. 3. 6. 1. 2. 3. 5. 2. 2. 1. 4.
0. 2. 2. 0. 3. 2.]
[ 1. 0. 1. 1. 5. 6. 1. 2. 1. 6. 0. 6. 1. 2.
3. 3. 1. 6. 2. 4.]
[ 0. 0. 1. 2. 0. 0. 1. 3. 2. 5. 2. 7. 0. 1.
0. 4. 6. 3. 0. 9.]
[ 0. 0. 3. 4. 2. 5. 5. 4. 2. 4. 2. 2. 0. 2.
0. 4. 6. 4. 1. 6.]
[ 1. 4. 5. 5. 1. 3. 3. 3. 1. 1. 3. 6. 2. 1.
2. 3. 1. 0. 3. 2.]
[ 2. 0. 2. 3. 1. 4. 1. 3. 2. 4. 4. 5. 0. 6.
0. 2. 2. 5. 3. 2.]
[ 0. 2. 2. 2. 1. 1. 8. 3. 2. 4. 1. 3. 0. 3.
3. 2. 2. 3. 4. 5.]
[ 1. 0. 1. 4. 2. 6. 7. 3. 1. 4. 2. 7. 0. 8.
2. 1. 3. 3. 1. 8.]
[ 2. 0. 1. 1. 1. 2. 4. 2. 2. 3. 3. 1. 1. 1.
1. 1. 1. 6. 2. 4.]
[ 2. 0. 0. 2. 1. 7. 4. 1. 0. 4. 3. 4. 0. 2.
4. 1. 1. 3. 2. 4.]
[ 1. 2. 1. 2. 3. 1. 2. 0. 1. 4. 2. 2. 1. 2.
0. 5. 4. 2. 3. 7.]
[ 2. 4. 2. 1. 2. 2. 7. 0. 1. 2. 0. 5. 5. 3.
2. 1. 3. 2. 4. 3.]
[ 0. 1. 0. 1. 5. 1. 1. 4. 1. 7. 6. 6. 0. 2.
0. 2. 2. 6. 2. 7.]
[ 2. 2. 4. 4. 2. 4. 4. 1. 1. 4. 0. 2. 6. 2.
6. 3. 5. 2. 5. 1.]
[ 2. 0. 2. 0. 9. 3. 1. 2. 0. 7. 4. 5. 0. 5.
0. 2. 0. 8. 2. 5.]
[ 7. 1. 5. 1. 3. 4. 1. 0. 4. 1. 1. 4. 2. 2.
6. 0. 2. 1. 5. 0.]
[ 0. 0. 1. 1. 5. 5. 5. 2. 1. 6. 1. 2. 1. 4.
4. 1. 0. 3. 2. 1.]
[ 0. 0. 1. 2. 3. 3. 4. 1. 0. 4. 5. 7. 0. 2.
3. 1. 3. 3. 1. 2.]
[ 1. 2. 1. 2. 3. 1. 1. 1. 3. 3. 1. 2. 1. 2.
4. 1. 5. 0. 1. 5.]
[ 5. 0. 2. 1. 2. 4. 4. 1. 1. 1. 5. 3. 1. 2.
1. 3. 2. 3. 1. 3.]
[ 2. 2. 2. 3. 1. 1. 2. 5. 1. 2. 2. 4. 3. 2.
5. 4. 4. 5. 6. 1.]
[ 2. 2. 3. 4. 0. 3. 2. 1. 3. 1. 0. 0. 1. 4.
2. 5. 1. 2. 2. 1.]
[ 4. 4. 2. 1. 3. 3. 3. 2. 1. 10. 0. 3. 8. 3.
0. 3. 2. 2. 3. 1.]
[ 4. 3. 1. 0. 4. 4. 3. 4. 0. 3. 5. 5. 3. 5.
5. 4. 2. 4. 1. 5.]
[ 0. 1. 1. 5. 2. 2. 1. 2. 3. 4. 2. 9. 0. 5.
1. 4. 9. 4. 0. 2.]
[ 1. 3. 2. 4. 0. 4. 1. 2. 3. 2. 2. 0. 3. 2.
3. 3. 5. 2. 4. 1.]
[ 1. 0. 4. 3. 1. 5. 2. 0. 1. 3. 2. 7. 0. 4.
1. 6. 2. 2. 3. 2.]
[ 0. 1. 3. 2. 3. 0. 4. 3. 2. 2. 3. 1. 4. 3.
2. 2. 1. 2. 3. 2.]
[ 2. 5. 1. 4. 0. 4. 3. 1. 2. 0. 3. 6. 2. 3.
4. 2. 3. 2. 2. 4.]
[ 1. 1. 3. 4. 4. 3. 0. 4. 4. 0. 4. 4. 4. 4.
3. 5. 4. 0. 3. 2.]
[ 2. 3. 0. 3. 3. 3. 3. 2. 0. 3. 1. 1. 4. 4.
3. 1. 1. 0. 0. 2.]
[ 0. 3. 3. 1. 2. 2. 3. 1. 2. 6. 4. 7. 0. 6.
0. 4. 5. 2. 1. 3.]
[ 1. 0. 2. 2. 4. 2. 2. 4. 3. 3. 5. 2. 0. 5.
1. 4. 1. 3. 2. 4.]
[ 5. 1. 2. 4. 2. 3. 2. 0. 2. 0. 2. 3. 3. 2.
1. 1. 4. 2. 1. 4.]
[ 1. 2. 0. 4. 2. 1. 1. 3. 3. 6. 1. 8. 0. 4.
0. 5. 6. 3. 0. 1.]
[ 0. 0. 4. 1. 4. 3. 1. 3. 0. 6. 3. 4. 0. 5.
2. 1. 1. 3. 2. 3.]
[ 0. 0. 2. 2. 2. 1. 0. 2. 0. 4. 3. 7. 2. 0.
0. 4. 6. 7. 0. 8.]
[ 1. 0. 0. 1. 4. 2. 4. 1. 0. 2. 2. 6. 1. 1.
0. 4. 2. 4. 6. 3.]
[ 1. 4. 7. 1. 1. 5. 1. 6. 0. 5. 1. 1. 5. 4.
0. 4. 0. 2. 1. 1.]
[ 2. 2. 2. 4. 4. 3. 4. 1. 4. 0. 2. 1. 4. 5.
3. 0. 2. 5. 4. 5.]
[ 6. 4. 6. 3. 2. 3. 1. 6. 2. 3. 2. 2. 3. 3.
2. 4. 2. 1. 5. 0.]
[ 0. 2. 1. 1. 1. 3. 2. 2. 1. 9. 1. 6. 0. 3.
3. 6. 1. 4. 5. 4.]
[ 1. 2. 1. 4. 5. 1. 1. 0. 6. 0. 2. 1. 5. 2.
3. 3. 4. 2. 4. 1.]
[ 1. 0. 0. 3. 5. 3. 4. 2. 1. 3. 3. 8. 0. 1.
0. 8. 5. 6. 2. 7.]
[ 0. 0. 3. 3. 2. 6. 0. 4. 1. 3. 4. 5. 0. 2.
0. 4. 3. 5. 1. 0.]
[ 0. 1. 1. 1. 3. 1. 0. 4. 0. 5. 3. 4. 0. 1.
0. 7. 3. 2. 0. 4.]
[ 0. 2. 1. 1. 3. 0. 0. 0. 3. 4. 3. 6. 0. 2.
0. 4. 5. 6. 0. 11.]
[ 3. 1. 3. 2. 4. 4. 7. 3. 0. 2. 3. 7. 2. 5.
1. 3. 2. 3. 1. 5.]
[ 1. 2. 2. 1. 1. 4. 4. 4. 1. 2. 5. 2. 4. 0.
3. 2. 1. 6. 2. 4.]
[ 0. 0. 1. 2. 3. 3. 1. 1. 0. 3. 1. 3. 2. 0.
2. 4. 5. 2. 2. 2.]
[ 2. 2. 4. 2. 3. 3. 1. 2. 4. 3. 2. 1. 1. 2.
2. 4. 4. 1. 2. 2.]]
[[0 0]
[0 0]
[0 1]
[0 1]
[1 0]
[0 0]
[1 0]
[0 0]
[0 0]
[1 1]
[1 0]
[1 0]
[0 1]
[1 1]
[0 0]
[0 0]
[0 0]
[0 1]
[0 0]
[0 1]
[0 1]
[1 1]
[0 0]
[1 0]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[1 1]
[1 1]
[0 1]
[0 0]
[1 0]
[0 1]
[0 0]
[0 1]
[0 0]
[0 0]
[0 1]
[0 1]
[1 0]
[0 0]
[0 0]
[0 0]
[0 1]
[0 0]
[0 1]
[0 0]
[0 0]
[1 1]
[1 0]
[1 1]
[0 0]
[0 1]
[0 0]
[0 1]
[0 1]
[0 1]
[1 1]
[0 0]
[1 1]
[0 0]
[0 1]
[0 0]
[0 1]
[0 1]
[0 0]
[0 1]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[0 0]
[0 1]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[0 1]
[0 0]
[1 1]
[0 1]
[1 0]
[0 1]
[0 0]
[0 0]
[0 0]
[0 1]
[0 0]
[1 1]
[0 1]
[1 0]
[1 0]
[0 1]
[0 0]
[1 1]
[0 0]]
In [56]:
a = np.random.rand(100)
In [58]:
b = np.random.rand(100)
In [59]:
%time euclidean(a,b)
CPU times: user 702 µs, sys: 4 µs, total: 706 µs
Wall time: 50.5 ms
Out[59]:
4.065469531638643
In [62]:
def dist(a,b):
return np.sqrt(np.dot(a, a) - 2 * np.dot(a, b) + np.dot(b, b))
In [96]:
a = np.zeros([2,9])
In [97]:
b = np.zeros([2,10])
In [99]:
np.hstack([a,b]).shape
Out[99]:
(2, 19)
In [ ]:
Content source: mayanks43/auto-tag
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