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| <h2 id="overview">Overview</h2> | | <h2 id="overview">Overview</h2> |
| <div class="overview"> | | <div class="overview"> |
− | <p>What make S-Din work are the algorithms behind it.</p> | + | <p>What make S-Din work are the algorithms behind it. |
− | <p>Our model group worked very hard to develop the recommend and simulation algorithms to make S-Din possible in functioning as expect.</p> | + | Our model group worked very hard to develop the recommend and simulation algorithms to make S-Din possible in functioning as expect. |
− | <p>We combined the search and recommend algorithm together so the software can work faster.</p> | + | We combined the search and recommend algorithm together so the software can work faster. |
− | <p>Simulation algorithm are developed to cover situations which is any circuits designed by users.</p> | + | Simulation algorithm are developed to cover situations which is any circuits designed by users.</p> |
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| <div class="paragraph"> | | <div class="paragraph"> |
| <p>Word2vec is an algorithm that produces word embedding , i.e. it converts a corpus of text into a high dimensional real vector space(in our case , the dimension is 400) and each word in the corpus is assigned to a vector in the vector space. If two words are similar semantically , then their will be close under cosine distance measure.</p> | | <p>Word2vec is an algorithm that produces word embedding , i.e. it converts a corpus of text into a high dimensional real vector space(in our case , the dimension is 400) and each word in the corpus is assigned to a vector in the vector space. If two words are similar semantically , then their will be close under cosine distance measure.</p> |
− | <p>In our Recommendation System, we use gensim, an open source Python module focused on Natural Language Processing , to train our word2vec model and the corpus we use to feed the model is wikimedia, which can be downloaded from:<a href="https://dumps.wikimedia.org/">https://dumps.wikimedia.org/</a></p> | + | <p>In our Recommendation System, we use gensim, an open source Python module focused on Natural Language Processing , to train our word2vec model and the corpus we use to feed the model is wikimedia, which can be downloaded from:<a href="https://dumps.wikimedia.org/" target="_blank">https://dumps.wikimedia.org/</a></p> |
| <p>The reason why we use Word2vec is that it can distinguish the semantic meanings of words accurately by Deep Learning technique, which outperforms the traditional semantic analysis methods greatly.</p> | | <p>The reason why we use Word2vec is that it can distinguish the semantic meanings of words accurately by Deep Learning technique, which outperforms the traditional semantic analysis methods greatly.</p> |
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| <img src="https://static.igem.org/mediawiki/2017/a/a8/T--SYSU-Software--modeling_law-of-cos2.png" alt="law of cosine" class="centered formula" id="law-of-cos2-img"> | | <img src="https://static.igem.org/mediawiki/2017/a/a8/T--SYSU-Software--modeling_law-of-cos2.png" alt="law of cosine" class="centered formula" id="law-of-cos2-img"> |
| <p>Now we see that the calculation of cosine distance between normalized vectors can be replaced by the calculation of Euclidean distance since they can determine each other in this normalization context.</p> | | <p>Now we see that the calculation of cosine distance between normalized vectors can be replaced by the calculation of Euclidean distance since they can determine each other in this normalization context.</p> |
− | <p>In our Recommendation System , we use the KD Tree implementation in scikit-learn , a simple and efficient open source machine learning module in Python. For more details of KD Tree algorithm, see: <a href=" https://en.wikipedia.org/wiki/K-d_tree">wiki page of K-D Tree</a></p> | + | <p>In our Recommendation System , we use the KD Tree implementation in scikit-learn , a simple and efficient open source machine learning module in Python. For more details of KD Tree algorithm, see: <a href=" https://en.wikipedia.org/wiki/K-d_tree" target="_blank">wiki page of K-D Tree</a></p> |
| </div> | | </div> |
| <h4>Random Walk with Restart Algorithm</h4> | | <h4>Random Walk with Restart Algorithm</h4> |
| <img src="https://static.igem.org/mediawiki/2017/c/c0/T--SYSU-Software--modeling_part-keyword.png" class="centered" alt="" id="keyword-part-img"> | | <img src="https://static.igem.org/mediawiki/2017/c/c0/T--SYSU-Software--modeling_part-keyword.png" class="centered" alt="" id="keyword-part-img"> |
− | <p>Random Walk with Restart(RWR) is an algorithm adapted from the PageRank algorithm and it focuses on characterizing the affiliation between each item. We treat the relation between key words and genetic parts as an undirected graph where nodes represent key words or parts and edges represent connection between words and parts. Imagine there is a walker travelling on the graph mentioned above and each time he faces two choices: 1) Randomly travelling along an edge connected to the current node. 2) Teleport to node K. After a long time of random travelling , the frequency the walker reaches each node represents the affiliation between each node and node K , which we use to characterize the relation between key words and parts. For more detailed mathematical formulation of PageRank, see: <a href="https://en.wikipedia.org/wiki/PageRank">wiki page of page rank</a></p> | + | <p>Random Walk with Restart(RWR) is an algorithm adapted from the PageRank algorithm and it focuses on characterizing the affiliation between each item. We treat the relation between key words and genetic parts as an undirected graph where nodes represent key words or parts and edges represent connection between words and parts. Imagine there is a walker travelling on the graph mentioned above and each time he faces two choices: 1) Randomly travelling along an edge connected to the current node. 2) Teleport to node K. After a long time of random travelling , the frequency the walker reaches each node represents the affiliation between each node and node K , which we use to characterize the relation between key words and parts. For more detailed mathematical formulation of PageRank, see: <a href="https://en.wikipedia.org/wiki/PageRank" target="_blank">wiki page of page rank</a></p> |
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| <h3>Algorithm to construct the Recommendation System</h3> | | <h3>Algorithm to construct the Recommendation System</h3> |
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| <p></p> | | <p></p> |
| <h3>Imlementation</h3> | | <h3>Imlementation</h3> |
− | <p>We use Scipy, an efficient open source numerical module in Python, to give the numerical solutions to the ODE system. Specifically, we use the odeint function in Scipy. For more information about odeint function, see <a href=" https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.odeint.html">documentation of scipy</a></p> | + | <p>We use Scipy, an efficient open source numerical module in Python, to give the numerical solutions to the ODE system. Specifically, we use the odeint function in Scipy. For more information about odeint function, see <a href=" https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.odeint.html" target="_blank">documentation of scipy</a></p> |
| </div> | | </div> |
| </section> | | </section> |