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comparison notebooks/test_hubness.ipynb @ 12:ff18f364bbac branch-tests

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author mpanteli <m.x.panteli@gmail.com>
date Tue, 12 Sep 2017 18:02:43 +0100
parents a1a9b472bcdb
children ed109218dd4b
comparison
equal deleted inserted replaced
11:a1a9b472bcdb 12:ff18f364bbac
23 "import scripts.utils_spatial as utils_spatial" 23 "import scripts.utils_spatial as utils_spatial"
24 ] 24 ]
25 }, 25 },
26 { 26 {
27 "cell_type": "code", 27 "cell_type": "code",
28 "execution_count": 4, 28 "execution_count": 2,
29 "metadata": {}, 29 "metadata": {},
30 "outputs": [ 30 "outputs": [
31 {
32 "name": "stderr",
33 "output_type": "stream",
34 "text": [
35 "/homes/mp305/anaconda/lib/python2.7/site-packages/pysal/weights/weights.py:189: UserWarning: There are 21 disconnected observations\n",
36 " warnings.warn(\"There are %d disconnected observations\" % ni)\n",
37 "/homes/mp305/anaconda/lib/python2.7/site-packages/pysal/weights/weights.py:190: UserWarning: Island ids: 3, 6, 26, 35, 39, 45, 52, 61, 62, 66, 77, 85, 94, 97, 98, 102, 103, 107, 110, 120, 121\n",
38 " warnings.warn(\"Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n"
39 ]
40 },
31 { 41 {
32 "name": "stdout", 42 "name": "stdout",
33 "output_type": "stream", 43 "output_type": "stream",
34 "text": [ 44 "text": [
35 "Antigua and Barbuda\n", 45 "Antigua and Barbuda\n",
68 "df_global, threshold, MD = results.get_outliers_df(X, Y, chi2thr=0.999)" 78 "df_global, threshold, MD = results.get_outliers_df(X, Y, chi2thr=0.999)"
69 ] 79 ]
70 }, 80 },
71 { 81 {
72 "cell_type": "code", 82 "cell_type": "code",
83 "execution_count": 3,
84 "metadata": {},
85 "outputs": [
86 {
87 "data": {
88 "text/plain": [
89 "(8200, 380)"
90 ]
91 },
92 "execution_count": 3,
93 "metadata": {},
94 "output_type": "execute_result"
95 }
96 ],
97 "source": [
98 "X.shape"
99 ]
100 },
101 {
102 "cell_type": "code",
103 "execution_count": 4,
104 "metadata": {},
105 "outputs": [],
106 "source": [
107 "D = pairwise_distances(X, metric='mahalanobis')"
108 ]
109 },
110 {
111 "cell_type": "code",
73 "execution_count": 5, 112 "execution_count": 5,
74 "metadata": {}, 113 "metadata": {},
75 "outputs": [ 114 "outputs": [
76 { 115 {
77 "data": { 116 "data": {
78 "text/plain": [ 117 "text/plain": [
79 "(8200, 380)" 118 "(8200, 8200)"
80 ] 119 ]
81 }, 120 },
82 "execution_count": 5, 121 "execution_count": 5,
83 "metadata": {}, 122 "metadata": {},
84 "output_type": "execute_result" 123 "output_type": "execute_result"
85 } 124 }
86 ], 125 ],
87 "source": [ 126 "source": [
88 "X.shape" 127 "D.shape"
89 ]
90 },
91 {
92 "cell_type": "code",
93 "execution_count": null,
94 "metadata": {},
95 "outputs": [],
96 "source": [
97 "D = pairwise_distances(X, metric='mahalanobis')"
98 ] 128 ]
99 }, 129 },
100 { 130 {
101 "cell_type": "code", 131 "cell_type": "code",
102 "execution_count": 6, 132 "execution_count": 6,
103 "metadata": {}, 133 "metadata": {},
104 "outputs": [ 134 "outputs": [
105 { 135 {
106 "data": { 136 "data": {
107 "image/png": 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137 "image/png": 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SnUleSnJ5p/7iJC+0ZXd16s9I8kCrfzrJBZ1la9tr7EhyQ6d+eZJnWpstSU7r4w2RJB2/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\n",
108 "text/plain": [ 138 "text/plain": [
109 "<matplotlib.figure.Figure at 0x1174100d0>" 139 "<matplotlib.figure.Figure at 0x7f3668585f50>"
110 ] 140 ]
111 }, 141 },
112 "metadata": {}, 142 "metadata": {},
113 "output_type": "display_data" 143 "output_type": "display_data"
114 } 144 }
117 "plt.hist(D.ravel(), bins=100);" 147 "plt.hist(D.ravel(), bins=100);"
118 ] 148 ]
119 }, 149 },
120 { 150 {
121 "cell_type": "code", 151 "cell_type": "code",
122 "execution_count": null, 152 "execution_count": 7,
123 "metadata": { 153 "metadata": {
124 "collapsed": true 154 "collapsed": true
125 }, 155 },
126 "outputs": [], 156 "outputs": [],
127 "source": [ 157 "source": [
134 " return N_k" 164 " return N_k"
135 ] 165 ]
136 }, 166 },
137 { 167 {
138 "cell_type": "code", 168 "cell_type": "code",
139 "execution_count": 18, 169 "execution_count": 8,
140 "metadata": {}, 170 "metadata": {},
141 "outputs": [ 171 "outputs": [
142 { 172 {
143 "name": "stdout", 173 "name": "stdout",
144 "output_type": "stream", 174 "output_type": "stream",
145 "text": [ 175 "text": [
146 "2.71175956476\n" 176 "8.18316065981\n"
147 ] 177 ]
148 }, 178 },
149 { 179 {
150 "data": { 180 "data": {
151 "image/png": 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152 "text/plain": [ 182 "text/plain": [
153 "<matplotlib.figure.Figure at 0x1e926a590>" 183 "<matplotlib.figure.Figure at 0x7f35fe65fe50>"
154 ] 184 ]
155 }, 185 },
156 "metadata": {}, 186 "metadata": {},
157 "output_type": "display_data" 187 "output_type": "display_data"
158 } 188 }
163 "plt.hist(N_k, bins=100);" 193 "plt.hist(N_k, bins=100);"
164 ] 194 ]
165 }, 195 },
166 { 196 {
167 "cell_type": "code", 197 "cell_type": "code",
168 "execution_count": 9, 198 "execution_count": 11,
169 "metadata": { 199 "metadata": {},
170 "collapsed": true 200 "outputs": [],
171 }, 201 "source": [
172 "outputs": [], 202 "#sort_idx = np.argsort(D, axis=1)\n",
173 "source": [ 203 "k = 10\n",
174 "N_k" 204 "D_k = sort_idx[:, 1:(k+1)]"
205 ]
206 },
207 {
208 "cell_type": "code",
209 "execution_count": 12,
210 "metadata": {},
211 "outputs": [
212 {
213 "data": {
214 "text/plain": [
215 "array([[4650, 2942, 3520, ..., 1318, 6678, 6056],\n",
216 " [1933, 6143, 6757, ..., 7269, 4321, 1563],\n",
217 " [3170, 2549, 4860, ..., 6678, 7414, 6056],\n",
218 " ..., \n",
219 " [6016, 2243, 1616, ..., 7627, 2018, 515],\n",
220 " [7027, 4860, 6346, ..., 997, 3892, 1846],\n",
221 " [5119, 1563, 4035, ..., 3486, 7617, 3854]])"
222 ]
223 },
224 "execution_count": 12,
225 "metadata": {},
226 "output_type": "execute_result"
227 }
228 ],
229 "source": [
230 "D_k"
175 ] 231 ]
176 }, 232 },
177 { 233 {
178 "cell_type": "code", 234 "cell_type": "code",
179 "execution_count": null, 235 "execution_count": null,