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others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. Many measures of image distance (Similarity Models) have been developed.
1328:). The first approach scored all amino acid changes equally. A later refinement was to determine amino acid similarities based on how many base changes were required to change a codon to code for that amino acid. This model is better, but it doesn't take into account the selective pressure of amino acid changes. Better models took into account the chemical properties of amino acids.
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The choice of similarity measure depends on the type of data being clustered and the specific problem being solved. For example, working with continuous data such as gene expression data, the
Euclidean distance or cosine similarity may be appropriate. If working with binary data such as the presence
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is a data mining technique that is used to discover patterns in data by grouping similar objects together. It involves partitioning a set of data points into groups or clusters based on their similarities. One of the fundamental aspects of clustering is how to measure similarity between data points.
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There are many various options available when it comes to finding similarity between two data points, some of which are a combination of other similarity methods. Some of the methods for similarity measures between two data points include
Euclidean distance, Manhattan distance, Minkowski distance,
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The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and
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or
Jaccard similarity, which is used in clustering techniques that work with binary data such as presence/absence data or Boolean data; The Jaccard similarity is particularly useful for clustering techniques that work with text data, where it can be used to identify clusters of similar documents
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such as A or G to another purine) than to transversions (from a pyrimidine to a purine or vice versa). The match/mismatch ratio of the matrix sets the target evolutionary distance. The +1/−3 DNA matrix used by BLASTN is best suited for finding matches between sequences that are 99% identical; a
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with values representing the similarity of any pair of targets. Then, by analyzing and comparing the values in the matrix, it is possible to match two targets to a user's preference or link users based on their marks. In this system, it is relevant to observe the value itself and the absolute
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Similarities among 162 Relevant
Nuclear Profile are tested using the Jaccard Similarity measure (see figure with heatmap). The Jaccard similarity of the nuclear profile ranges from 0 to 1, with 0 indicating no similarity between the two sets and 1 indicating perfect similarity with the aim of
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Similarity measures play a crucial role in many clustering techniques, as they are used to determine how closely related two data points are and whether they should be grouped together in the same cluster. A similarity measure can take many different forms depending on the type of data being
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series of matrices. PAM matrices are labelled based on how many nucleotide changes have occurred, per 100 amino acids. While the PAM matrices benefit from having a well understood evolutionary model, they are most useful at short evolutionary distances (PAM10–PAM120). At long evolutionary
613:, is a commonly used similarity measure in clustering techniques that work with continuous data. It is a measure of the distance between two data points in a high-dimensional space, calculated as the sum of the absolute differences between the corresponding coordinates of the two points
323:. The Euclidean distance is a measure of the straight-line distance between two points in a high-dimensional space. It is calculated as the square root of the sum of the squared differences between the corresponding coordinates of the two points. For example, if we have two data points
706:(or similarity) is a common choice as it can handle different types of variables implicitly. It first computes similarities between the pair of variables in each object, and then combines those similarities to a single weighted average per object-pair. As such, for two objects
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of a genomic loci in a nuclear profile, the
Jaccard index may be more appropriate. Lastly, working with data that is arranged in a grid or lattice structure, such as image or signal processing data, the Manhattan distance is particularly useful for the clustering.
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distance between two values. Gathering this data can indicate a mark's likeliness to a user as well as how mutually closely two marks are either rejected or accepted. It is possible then to recommend to a user targets with high similarity to the user's likes.
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that can be used. Some of these methods include edit distance, Levenshtein distance, Hamming distance, and Jaro distance. The best-fit formula is dependent on the requirements of the application. For example, edit distance is frequently used for
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The BLOSUM series were generated by comparing a number of divergent sequences. The BLOSUM series are labeled based on how much entropy remains unmutated between all sequences, so a lower BLOSUM number corresponds to a higher PAM number.
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similarity matrices. For example, a simple matrix will assign identical bases a score of +1 and non-identical bases a score of −1. A more complicated matrix would give a higher score to transitions (changes from a
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Recommender systems are observed in multiple online entertainment platforms, in social media and streaming websites. The logic for the construction of this systems is based on similarity measures.
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61:: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms.
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and
Chebyshev distance. The Euclidean distance formula is used to find the distance between two points on a plane, which is visualized in the image below. Manhattan distance is commonly used in
206:. Both provide a quantification of similarity for two probability distributions on the same domain, and they are mathematically closely linked. The Bhattacharyya distance does not fulfill the
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also compares the number of items in both sets to the total number of items present but the weight for the number of shared items is larger. The Sørensen–Dice coefficient is commonly used in
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Different types of similarity measures exist for various types of objects, depending on the objects being compared. For each type of object there are various similarity measurement formulas.
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applications, as it can be used to find the shortest route between two addresses. When you generalize the
Euclidean distance formula and Manhattan distance formula you are left with the
1013:, a similarity, or affinity, measure is used to transform data to overcome difficulties related to lack of convexity in the shape of the data distribution. The measure gives rise to an
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that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of
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When comparing temporal sequences (time series), some similarity measures must additionally account for similarity of two sequences that are not fully aligned.
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based on their shared features or keywords. It is calculated as the size of the intersection of two sets divided by the size of the union of the two sets:
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International MultiConference of
Engineers and Computer Scientists : IMECS 2013 : 13-15 March, 2013, the Royal Garden Hotel, Kowloon, Hong Kong
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applications and features, such as spell-checking. Jaro distance is commonly used in record linkage to compare first and last names to other sources.
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1324:, and so a larger number of possible substitutions. Therefore, the similarity matrix for amino acids contains 400 entries (although it is usually
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States, D; Gish, W; Altschul, S (1991). "Improved sensitivity of nucleic acid database searches using application-specific scoring matrices".
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1230:. It observes a user's perception and liking of multiple items. On recommender systems, the method is using a distance calculation such as
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based on the number of items that are present in both sets relative to the total number of items. It is commonly used in
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Li, Xin-Ye; Guo, Li-Jie (2012), "Constructing affinity matrix in spectral clustering based on neighbor propagation",
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method used phylogenetic trees and sequences taken from species on the tree. This approach has given rise to the
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Heatmap of HIST1 region, which is located on mouse chromosome 13 at the following coordinates: .
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1503:"Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data"
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similarity matrices are more complicated, because there are 20 amino acids coded for by the
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is a commonly used similarity measure for real-valued vectors, used in (among other fields)
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913:{\displaystyle S_{ij}={\frac {\sum _{k=1}^{p}w_{ijk}s_{ijk}}{\sum _{k=1}^{p}w_{ijk}}},}
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214:. The Hellinger distance does form a metric on the space of probability distributions.
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692:{\displaystyle \left\vert x_{1}-x_{2}\right\vert +\left\vert y_{1}-y_{2}\right\vert }
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1565:. S. I. Ao, International Association of Engineers. Hong Kong: Newswood Ltd. 2013.
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Chung, Neo
Christopher; Miasojedow, BłaŻej; Startek, Michał; Gambin, Anna (2019).
1214:. Further modifying this result with network analysis techniques is also common.
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Content-based image retrieval § Content comparison using image distance measures
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applications, measuring the similarity between two sets of genes or species.
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Image shows the path of calculation when using the
Euclidean distance formula
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One approach has been to empirically generate the similarity matrices. The
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distances, for example PAM250 or 20% identity, it has been shown that the
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30:"Similarity matrix" redirects here. For the linear algebra concept, see
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Eidenberger, Horst (2011). "Fundamental Media Understanding", atpress.
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1133:, or it can be a more complex measure of distance such as the Gaussian
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1446:, a visualization tool of recurrences in dynamical (and other) systems
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sequences. Because there are only four nucleotides commonly found in
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Real-valued function that quantifies similarity between two objects
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formulas, which can be used in a wide variety of applications.
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1300:(T)), nucleotide similarity matrices are much simpler than
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is the similarity between the two objects regarding their
1722:"Where did the BLOSUM62 alignment score matrix come from?"
1440: – Estimate of the importance of a word in a document
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One of the most commonly used similarity measures is the
315:, which is used in many clustering techniques including
1207:{\displaystyle e^{-\|s_{1}-s_{2}\|^{2}/2\sigma ^{2}}}
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For comparing strings, there are various measures of
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Pages displaying wikidata descriptions as a fallback
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in the matrix can be simply the (reciprocal of the)
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1613:"On Spectral Clustering: Analysis and an Algorithm"
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1617:Advances in Neural Information Processing Systems
1489:https://iq.opengenus.org/similarity-measurements/
1434: – Searching for similar items in a data set
308:clustered and the specific problem being solved.
1422: – Distance between two statistical objects
1309:such as C or T to another pyrimidine, or from a
533:Another commonly used similarity measure is the
295:Hierarchical clustering § Similarity metric
190:Similarity between two probability distributions
1416: – Relation of resemblance between objects
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1685:Methods: A Companion to Methods in Enzymology
606:clustering the most similar nuclear profile.
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1491:"Different Types of Similarity measurements"
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238:formula measures the similarity between two
91:Use of different similarity measure formulas
71:to score the similarity of documents in the
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1820:F. Gregory Ashby; Daniel M. Ennis (2007).
1611:Ng, A.Y.; Jordan, M.I.; Weiss, Y. (2001),
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1784:. Upper Saddle River, NJ: Prentice Hall.
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1663:Similarity metrics in recommender systems
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1276:similarity matrices are used to align
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1401: – Natural language processing
609:Manhattan distance, also known as
194:Typical measures of similarity for
1463:Vert, Jean-Philippe; Tsuda, Koji;
1344:matrices are much more effective.
99:Similarity between two data points
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1378: – Algorithm in data mining
1357:This section is an excerpt from
1266:Similarity matrices are used in
274:Similarity between two sequences
2233:Pearson correlation coefficient
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953:are non-negative weights and
408:{\displaystyle (x_{2},y_{2})}
362:{\displaystyle (x_{1},y_{1})}
210:, meaning it does not form a
1648:10.1016/j.neucom.2012.06.023
1469:"A primer on kernel methods"
1405:Similarity (network science)
766:descriptors, the similarity
2016:Sensitivity and specificity
1660:Bondarenko, Kirill (2019),
230:Similarity between two sets
157:natural language processing
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2272:Statistical classification
1780:; George Stockman (2001).
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1222:Use in recommender systems
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146:Similarity between strings
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1847:10.4249/scholarpedia.4116
1520:10.1186/s12859-019-3118-5
1262:Use in sequence alignment
268:Sørensen–Dice coefficient
252:Sørensen–Dice coefficient
196:probability distributions
1057:points, where the entry
515:{\displaystyle d=\surd }
2044:Calinski-Harabasz index
1414:Similarity (philosophy)
979:{\displaystyle s_{ijk}}
946:{\displaystyle w_{ijk}}
321:Hierarchical clustering
1593:: CS1 maint: others (
1394:Self-similarity matrix
1352:Use in computer vision
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244:recommendation systems
219:Bhattacharyya distance
200:Bhattacharyya distance
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41:and related fields, a
2207:Intra-list Similarity
1822:"Similarity measures"
1720:Sean R. Eddy (2004).
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2277:Statistical distance
1741:10.1038/nbt0804-1035
1729:Nature Biotechnology
1623:, MIT Press: 849–856
1420:Statistical distance
1376:Affinity propagation
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2267:Clustering criteria
1838:2007SchpJ...2.4116A
1465:Schölkopf, Bernhard
1399:Semantic similarity
1388:Similarity learning
1228:recommender systems
1011:spectral clustering
208:triangle inequality
47:similarity function
18:Similarity function
2228:Euclidean distance
2194:Recommender system
2074:Similarity measure
1888:evaluation metrics
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1268:sequence alignment
1234:Euclidean Distance
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204:Hellinger distance
140:Chebyshev distance
135:Minkowski distance
130:Manhattan distance
125:Euclidean distance
118:Minkowski distance
109:
73:vector space model
43:similarity measure
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2223:Cosine similarity
2059:Hopkins statistic
1810:978-3-8423-7917-6
1791:978-0-13-030796-5
1572:978-988-19251-8-3
1432:Similarity search
1250:similarity matrix
1242:Cosine Similarity
1126:{\displaystyle j}
1106:{\displaystyle i}
1049:similarity matrix
999:{\displaystyle k}
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779:{\displaystyle S}
759:{\displaystyle p}
739:{\displaystyle j}
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289:Use in clustering
152:string similarity
65:Cosine similarity
51:similarity metric
32:Matrix similarity
16:(Redirected from
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2246:Confusion matrix
2021:Logarithmic Loss
1886:Machine learning
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2295:
2293:
2285:
2284:
2279:
2274:
2269:
2259:
2258:
2252:
2251:
2249:
2248:
2242:
2239:
2238:
2236:
2235:
2230:
2225:
2219:
2217:
2213:
2212:
2210:
2209:
2204:
2198:
2196:
2190:
2189:
2187:
2186:
2181:
2175:
2173:
2169:
2168:
2166:
2165:
2160:
2154:
2152:
2146:
2145:
2143:
2142:
2137:
2132:
2126:
2124:
2118:
2117:
2115:
2114:
2109:
2104:
2098:
2096:
2090:
2089:
2087:
2086:
2081:
2076:
2071:
2066:
2061:
2056:
2051:
2049:Davies-Bouldin
2046:
2041:
2035:
2033:
2027:
2026:
2024:
2023:
2018:
2013:
2008:
2003:
1998:
1993:
1988:
1983:
1978:
1973:
1967:
1965:
1963:Classification
1959:
1958:
1956:
1955:
1950:
1945:
1940:
1935:
1930:
1925:
1920:
1915:
1910:
1905:
1899:
1897:
1891:
1890:
1884:
1882:
1881:
1874:
1867:
1859:
1853:
1852:
1815:
1814:
1797:
1790:
1778:Shapiro, Linda
1769:
1766:on 2006-09-03.
1712:
1675:
1652:
1636:Neurocomputing
1626:
1600:
1571:
1552:
1493:
1481:
1454:
1452:
1449:
1448:
1447:
1441:
1435:
1429:
1423:
1417:
1411:
1402:
1396:
1391:
1385:
1379:
1371:
1368:
1363:
1355:
1353:
1350:
1263:
1260:
1246:to generate a
1223:
1220:
1199:
1195:
1191:
1187:
1181:
1177:
1171:
1167:
1163:
1158:
1154:
1150:
1147:
1143:
1122:
1102:
1078:
1075:
1072:
1069:
1066:
1034:
1031:
1028:
1025:
1022:
1006:-th variable.
995:
973:
970:
967:
963:
940:
937:
934:
930:
909:
901:
898:
895:
891:
885:
880:
877:
874:
870:
862:
859:
856:
852:
846:
843:
840:
836:
830:
825:
822:
819:
815:
808:
803:
800:
796:
775:
755:
735:
715:
687:
681:
677:
673:
668:
664:
659:
655:
651:
645:
641:
637:
632:
628:
623:
588:
585:
582:
577:
574:
571:
565:
562:
559:
556:
553:
550:
547:
511:
506:
502:
496:
492:
488:
483:
479:
475:
472:
467:
463:
457:
453:
449:
444:
440:
436:
433:
430:
427:
424:
404:
399:
395:
391:
386:
382:
378:
358:
353:
349:
345:
340:
336:
332:
299:Clustering or
290:
287:
286:
285:
271:
270:
265:
227:
226:
221:
187:
186:
181:
176:
171:
166:
143:
142:
137:
132:
127:
92:
89:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
2294:
2283:
2280:
2278:
2275:
2273:
2270:
2268:
2265:
2264:
2262:
2247:
2244:
2243:
2240:
2234:
2231:
2229:
2226:
2224:
2221:
2220:
2218:
2214:
2208:
2205:
2203:
2200:
2199:
2197:
2195:
2191:
2185:
2182:
2180:
2177:
2176:
2174:
2170:
2164:
2161:
2159:
2156:
2155:
2153:
2151:
2147:
2141:
2138:
2136:
2133:
2131:
2128:
2127:
2125:
2123:
2119:
2113:
2110:
2108:
2105:
2103:
2100:
2099:
2097:
2095:
2091:
2085:
2082:
2080:
2077:
2075:
2072:
2070:
2067:
2065:
2064:Jaccard index
2062:
2060:
2057:
2055:
2052:
2050:
2047:
2045:
2042:
2040:
2037:
2036:
2034:
2032:
2028:
2022:
2019:
2017:
2014:
2012:
2009:
2007:
2004:
2002:
1999:
1997:
1994:
1992:
1989:
1987:
1984:
1982:
1979:
1977:
1974:
1972:
1969:
1968:
1966:
1964:
1960:
1954:
1951:
1949:
1946:
1944:
1941:
1939:
1936:
1934:
1931:
1929:
1926:
1924:
1921:
1919:
1916:
1914:
1911:
1909:
1906:
1904:
1901:
1900:
1898:
1896:
1892:
1887:
1880:
1875:
1873:
1868:
1866:
1861:
1860:
1857:
1848:
1843:
1839:
1835:
1831:
1827:
1823:
1818:
1817:
1811:
1807:
1801:
1798:
1793:
1787:
1783:
1779:
1773:
1770:
1762:
1758:
1754:
1750:
1746:
1742:
1738:
1735:(8): 1035–6.
1734:
1730:
1723:
1716:
1713:
1708:
1704:
1699:
1694:
1690:
1686:
1679:
1676:
1665:
1664:
1656:
1653:
1649:
1645:
1641:
1637:
1630:
1627:
1622:
1618:
1614:
1607:
1605:
1601:
1596:
1590:
1582:
1578:
1574:
1568:
1564:
1563:
1556:
1553:
1548:
1544:
1539:
1534:
1530:
1526:
1521:
1516:
1512:
1508:
1504:
1497:
1494:
1490:
1485:
1482:
1477:
1470:
1466:
1459:
1456:
1450:
1445:
1442:
1439:
1436:
1433:
1430:
1427:
1426:String metric
1424:
1421:
1418:
1415:
1412:
1406:
1403:
1400:
1397:
1395:
1392:
1389:
1386:
1383:
1380:
1377:
1374:
1373:
1369:
1360:
1351:
1349:
1345:
1343:
1338:
1334:
1329:
1327:
1323:
1319:
1315:
1312:
1308:
1303:
1299:
1295:
1291:
1287:
1283:
1279:
1275:
1271:
1269:
1261:
1259:
1256:
1253:
1245:
1237:
1229:
1221:
1219:
1215:
1197:
1193:
1189:
1185:
1179:
1169:
1165:
1161:
1156:
1152:
1145:
1141:
1120:
1100:
1092:
1073:
1070:
1067:
1053:for a set of
1052:
1029:
1026:
1023:
1012:
1007:
993:
971:
968:
965:
961:
938:
935:
932:
928:
907:
899:
896:
893:
889:
883:
878:
875:
872:
868:
860:
857:
854:
850:
844:
841:
838:
834:
828:
823:
820:
817:
813:
806:
801:
798:
794:
773:
753:
733:
713:
705:
700:
685:
679:
675:
671:
666:
662:
657:
653:
649:
643:
639:
635:
630:
626:
621:
612:
607:
603:
586:
583:
580:
575:
572:
569:
563:
557:
554:
551:
545:
536:
535:Jaccard index
527:
523:
504:
494:
490:
486:
481:
477:
470:
465:
455:
451:
447:
442:
438:
425:
422:
397:
393:
389:
384:
380:
351:
347:
343:
338:
334:
322:
318:
314:
309:
305:
302:
296:
288:
284:
281:
280:
279:
276:
275:
269:
266:
264:
263:Jaccard index
261:
260:
259:
257:
253:
249:
245:
241:
237:
236:Jaccard index
232:
231:
225:
222:
220:
217:
216:
215:
213:
209:
205:
201:
197:
192:
191:
185:
184:Jaro distance
182:
180:
177:
175:
172:
170:
167:
165:
164:Edit distance
162:
161:
160:
158:
153:
148:
147:
141:
138:
136:
133:
131:
128:
126:
123:
122:
121:
119:
115:
105:
101:
100:
96:
90:
88:
86:
82:
78:
74:
70:
66:
62:
60:
56:
52:
48:
44:
40:
33:
19:
2073:
1832:(12): 4116.
1829:
1826:Scholarpedia
1825:
1800:
1781:
1772:
1761:the original
1732:
1728:
1715:
1688:
1684:
1678:
1667:, retrieved
1662:
1655:
1639:
1635:
1629:
1620:
1616:
1561:
1555:
1513:(S15): 644.
1510:
1506:
1496:
1484:
1475:
1458:
1382:Latent space
1346:
1330:
1322:genetic code
1316:
1278:nucleic acid
1272:
1265:
1257:
1247:
1239:
1231:
1225:
1216:
1046:
1008:
701:
608:
604:
532:
310:
306:
298:
277:
273:
272:
233:
229:
228:
193:
189:
188:
174:Lee distance
149:
145:
144:
110:
98:
97:
94:
83:such as the
63:
50:
46:
42:
36:
1642:: 125–130,
2261:Categories
2216:Similarity
2158:Perplexity
2069:Rand index
2054:Dunn index
2039:Silhouette
2031:Clustering
1895:Regression
1451:References
1318:Amino acid
1307:pyrimidine
1274:Nucleotide
920:where the
293:See also:
85:RBF kernel
39:statistics
1986:Precision
1938:RMSE/RMSD
1757:205269887
1693:CiteSeerX
1691:(1): 66.
1589:cite book
1581:842831996
1529:1471-2105
1326:symmetric
1194:σ
1176:‖
1162:−
1149:‖
1146:−
869:∑
814:∑
672:−
636:−
584:⋃
573:⋂
487:−
448:−
429:√
79:, common
2202:Coverage
1981:Accuracy
1749:15286655
1669:25 April
1547:31874610
1467:(2004).
1370:See also
1296:(G) and
1290:Cytosine
1093:between
746:having
202:and the
198:are the
2094:Ranking
2084:SimHash
1971:F-score
1834:Bibcode
1538:6929325
1333:Dayhoff
1302:protein
1298:Thymine
1294:Guanine
1286:Adenine
1045:-sized
256:biology
1991:Recall
1808:
1788:
1755:
1747:
1695:
1579:
1569:
1545:
1535:
1527:
1438:tf–idf
1342:BLOSUM
1311:purine
250:. The
212:metric
1996:Kappa
1913:sMAPE
1764:(PDF)
1753:S2CID
1725:(PDF)
1472:(PDF)
1292:(C),
1288:(A),
75:. In
53:is a
2163:BLEU
2135:SSIM
2130:PSNR
2107:NDCG
1928:MSPE
1923:MASE
1918:MAPE
1806:ISBN
1786:ISBN
1745:PMID
1671:2023
1595:link
1577:OCLC
1567:ISBN
1543:PMID
1525:ISSN
1113:and
726:and
369:and
319:and
246:and
240:sets
234:The
2184:FID
2150:NLP
2140:IoU
2102:MRR
2079:SMC
2011:ROC
2006:AUC
2001:MCC
1953:MAD
1948:MDA
1933:RMS
1908:MAE
1903:MSE
1842:doi
1737:doi
1703:doi
1644:doi
1533:PMC
1515:doi
1337:PAM
1282:DNA
1238:or
1009:In
114:GPS
49:or
45:or
37:In
2263::
2112:AP
1976:P4
1840:.
1828:.
1824:.
1751:.
1743:.
1733:22
1731:.
1727:.
1701:.
1687:.
1640:97
1638:,
1621:14
1619:,
1615:,
1603:^
1591:}}
1587:{{
1575:.
1541:.
1531:.
1523:.
1511:20
1509:.
1505:.
1474:.
699:.
602:.
522:.
1943:R
1878:e
1871:t
1864:v
1850:.
1844::
1836::
1830:2
1812:.
1794:.
1739::
1709:.
1705::
1689:3
1646::
1597:)
1583:.
1549:.
1517::
1478:.
1361:.
1284:(
1198:2
1190:2
1186:/
1180:2
1170:2
1166:s
1157:1
1153:s
1142:e
1121:j
1101:i
1077:)
1074:j
1071:,
1068:i
1065:(
1055:n
1033:)
1030:n
1027:,
1024:n
1021:(
994:k
972:k
969:j
966:i
962:s
939:k
936:j
933:i
929:w
908:,
900:k
897:j
894:i
890:w
884:p
879:1
876:=
873:k
861:k
858:j
855:i
851:s
845:k
842:j
839:i
835:w
829:p
824:1
821:=
818:k
807:=
802:j
799:i
795:S
774:S
754:p
734:j
714:i
686:|
680:2
676:y
667:1
663:y
658:|
654:+
650:|
644:2
640:x
631:1
627:x
622:|
587:B
581:A
576:B
570:A
564:=
561:)
558:B
555:,
552:A
549:(
546:J
510:]
505:2
501:)
495:1
491:y
482:2
478:y
474:(
471:+
466:2
462:)
456:1
452:x
443:2
439:x
435:(
432:[
426:=
423:d
403:)
398:2
394:y
390:,
385:2
381:x
377:(
357:)
352:1
348:y
344:,
339:1
335:x
331:(
34:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
