Older blog entries for sness (starting at number 5002)

Optimal substructure - Wikipedia, the free encyclopedia

Optimal substructure - Wikipedia, the free encyclopedia: "In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed efficiently from optimal solutions of its subproblems. This property is used to determine the usefulness of dynamic programming and greedy algorithms for a problem.[1]"

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Syndicated 2013-01-25 17:50:00 from sness

Redesigning Google: how Larry Page engineered a beautiful revolution | The Verge

Redesigning Google: how Larry Page engineered a beautiful revolution | The Verge: "Something strange and remarkable started happening at Google immediately after Larry Page took full control as CEO in 2011: it started designing good-looking apps."

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Syndicated 2013-01-25 17:38:00 from sness

Needleman-Wunsch Algorithm for Global Sequence Alignment in Python - CodesOfMyLife

Needleman-Wunsch Algorithm for Global Sequence Alignment in Python - CodesOfMyLife: " Great! So I decided to start from beginning of the chain by learning some dynamic programming methods as a prelude to my holy grail (not really!). And first in the chain came Needleman-Wunsch method for global pairwise alignment."

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Syndicated 2013-01-25 17:12:00 from sness

Sequence alignment - Wikipedia, the free encyclopedia

Sequence alignment - Wikipedia, the free encyclopedia: "In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns."

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Syndicated 2013-01-25 16:38:00 from sness

python - Breadth-first search trace path - Stack Overflow

python - Breadth-first search trace path - Stack Overflow: "# graph is in adjacent list representation
graph = {
'1': ['2', '3', '4'],
'2': ['5', '6'],
'5': ['9', '10'],
'4': ['7', '8'],
'7': ['11', '12']
}

def bfs(graph, start, end):
# maintain a queue of paths
queue = []
# push the first path into the queue
queue.append([start])
while queue:
# get the first path from the queue
path = queue.pop(0)
# get the last node from the path
node = path[-1]
# path found
if node == end:
return path
# enumerate all adjacent nodes, construct a new path and push it into the queue
for adjacent in graph.get(node, []):
new_path = list(path)
new_path.append(adjacent)
queue.append(new_path)

print bfs(graph, '1', '11')"

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Syndicated 2013-01-24 23:20:00 from sness

A New Tune Tuesday - Episode 7 - YouTube

A New Tune Tuesday - Episode 7 - YouTube: "