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FULLTEXT01.pdf
1.1 MB
Application of Machine Learning to Financial Trading
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PHP and MySQL Web Development - Fifth Edition.pdf
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PHP and MySQL Web Development - Fifth Edition
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Designing Machine Learning Systems.pdf
6.6 MB
Designing Machine Learning Systems (2022)
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@maghalecomputer.pdf
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Machine Learning Bookcamp Build a portfolio of real-life pr.pdf
40 MB
Machine Learning Bookcamp (2021)
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کد الگوریتم دایجکسترا
class Graph():
def init(self, vertices):
self.V = vertices
self.graph = [[0 for column in range(vertices)]
for row in range(vertices)]
def printSolution(self, dist):
print("Vertex \t Distance from Source")
for node in range(self.V):
print(node, "\t\t", dist[node])
# A utility function to find the vertex with
# minimum distance value, from the set of vertices
# not yet included in shortest path tree
def minDistance(self, dist, sptSet):
# Initialize minimum distance for next node
min = 1e7
# Search not nearest vertex not in the
# shortest path tree
for v in range(self.V):
if dist[v] < min and sptSet[v] == False:
min = dist[v]
min_index = v
return min_index
# Function that implements Dijkstra's single source
# shortest path algorithm for a graph represented
# using adjacency matrix representation
def dijkstra(self, src):
dist = [1e7] * self.V
dist[src] = 0
sptSet = [False] * self.V
for cout in range(self.V):
# Pick the minimum distance vertex from
# the set of vertices not yet processed.
# u is always equal to src in first iteration
u = self.minDistance(dist, sptSet)
# Put the minimum distance vertex in the
# shortest path tree
sptSet[u] = True
# Update dist value of the adjacent vertices
# of the picked vertex only if the current
class Graph():
def init(self, vertices):
self.V = vertices
self.graph = [[0 for column in range(vertices)]
for row in range(vertices)]
def printSolution(self, dist):
print("Vertex \t Distance from Source")
for node in range(self.V):
print(node, "\t\t", dist[node])
# A utility function to find the vertex with
# minimum distance value, from the set of vertices
# not yet included in shortest path tree
def minDistance(self, dist, sptSet):
# Initialize minimum distance for next node
min = 1e7
# Search not nearest vertex not in the
# shortest path tree
for v in range(self.V):
if dist[v] < min and sptSet[v] == False:
min = dist[v]
min_index = v
return min_index
# Function that implements Dijkstra's single source
# shortest path algorithm for a graph represented
# using adjacency matrix representation
def dijkstra(self, src):
dist = [1e7] * self.V
dist[src] = 0
sptSet = [False] * self.V
for cout in range(self.V):
# Pick the minimum distance vertex from
# the set of vertices not yet processed.
# u is always equal to src in first iteration
u = self.minDistance(dist, sptSet)
# Put the minimum distance vertex in the
# shortest path tree
sptSet[u] = True
# Update dist value of the adjacent vertices
# of the picked vertex only if the current
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Yves-Hilpisch-Python-for-Finance_-Mastering-Data-Driven-Fina.pdf
6.2 MB
:Python for Finance: Mastering Data-Driven Finance Book
Author(s): Yves Hilpisch
Publisher: O'Reilly
Year: 2018
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Author(s): Yves Hilpisch
Publisher: O'Reilly
Year: 2018
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