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gboeing/osmnx | osmnx/core.py | add_paths | def add_paths(G, paths, bidirectional=False):
"""
Add a collection of paths to the graph.
Parameters
----------
G : networkx multidigraph
paths : dict
the paths from OSM
bidirectional : bool
if True, create bidirectional edges for one-way streets
Returns
-------
... | python | def add_paths(G, paths, bidirectional=False):
"""
Add a collection of paths to the graph.
Parameters
----------
G : networkx multidigraph
paths : dict
the paths from OSM
bidirectional : bool
if True, create bidirectional edges for one-way streets
Returns
-------
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gboeing/osmnx | osmnx/core.py | create_graph | def create_graph(response_jsons, name='unnamed', retain_all=False, bidirectional=False):
"""
Create a networkx graph from OSM data.
Parameters
----------
response_jsons : list
list of dicts of JSON responses from from the Overpass API
name : string
the name of the graph
reta... | python | def create_graph(response_jsons, name='unnamed', retain_all=False, bidirectional=False):
"""
Create a networkx graph from OSM data.
Parameters
----------
response_jsons : list
list of dicts of JSON responses from from the Overpass API
name : string
the name of the graph
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gboeing/osmnx | osmnx/core.py | bbox_from_point | def bbox_from_point(point, distance=1000, project_utm=False, return_crs=False):
"""
Create a bounding box some distance in each direction (north, south, east,
and west) from some (lat, lng) point.
Parameters
----------
point : tuple
the (lat, lon) point to create the bounding box around... | python | def bbox_from_point(point, distance=1000, project_utm=False, return_crs=False):
"""
Create a bounding box some distance in each direction (north, south, east,
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gboeing/osmnx | osmnx/core.py | graph_from_bbox | def graph_from_bbox(north, south, east, west, network_type='all_private',
simplify=True, retain_all=False, truncate_by_edge=False,
name='unnamed', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, clean_periphery=True,
infrastr... | python | def graph_from_bbox(north, south, east, west, network_type='all_private',
simplify=True, retain_all=False, truncate_by_edge=False,
name='unnamed', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, clean_periphery=True,
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gboeing/osmnx | osmnx/core.py | graph_from_point | def graph_from_point(center_point, distance=1000, distance_type='bbox',
network_type='all_private', simplify=True, retain_all=False,
truncate_by_edge=False, name='unnamed', timeout=180,
memory=None, max_query_area_size=50*1000*50*1000,
... | python | def graph_from_point(center_point, distance=1000, distance_type='bbox',
network_type='all_private', simplify=True, retain_all=False,
truncate_by_edge=False, name='unnamed', timeout=180,
memory=None, max_query_area_size=50*1000*50*1000,
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gboeing/osmnx | osmnx/core.py | graph_from_address | def graph_from_address(address, distance=1000, distance_type='bbox',
network_type='all_private', simplify=True, retain_all=False,
truncate_by_edge=False, return_coords=False,
name='unnamed', timeout=180, memory=None,
max_query_a... | python | def graph_from_address(address, distance=1000, distance_type='bbox',
network_type='all_private', simplify=True, retain_all=False,
truncate_by_edge=False, return_coords=False,
name='unnamed', timeout=180, memory=None,
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gboeing/osmnx | osmnx/core.py | graph_from_polygon | def graph_from_polygon(polygon, network_type='all_private', simplify=True,
retain_all=False, truncate_by_edge=False, name='unnamed',
timeout=180, memory=None,
max_query_area_size=50*1000*50*1000,
clean_periphery=True, infrastruc... | python | def graph_from_polygon(polygon, network_type='all_private', simplify=True,
retain_all=False, truncate_by_edge=False, name='unnamed',
timeout=180, memory=None,
max_query_area_size=50*1000*50*1000,
clean_periphery=True, infrastruc... | [
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gboeing/osmnx | osmnx/core.py | graph_from_place | def graph_from_place(query, network_type='all_private', simplify=True,
retain_all=False, truncate_by_edge=False, name='unnamed',
which_result=1, buffer_dist=None, timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, clean_periphery=True,
... | python | def graph_from_place(query, network_type='all_private', simplify=True,
retain_all=False, truncate_by_edge=False, name='unnamed',
which_result=1, buffer_dist=None, timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, clean_periphery=True,
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gboeing/osmnx | osmnx/core.py | graph_from_file | def graph_from_file(filename, bidirectional=False, simplify=True,
retain_all=False, name='unnamed'):
"""
Create a networkx graph from OSM data in an XML file.
Parameters
----------
filename : string
the name of a file containing OSM XML data
bidirectional : bool
... | python | def graph_from_file(filename, bidirectional=False, simplify=True,
retain_all=False, name='unnamed'):
"""
Create a networkx graph from OSM data in an XML file.
Parameters
----------
filename : string
the name of a file containing OSM XML data
bidirectional : bool
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gboeing/osmnx | osmnx/footprints.py | osm_footprints_download | def osm_footprints_download(polygon=None, north=None, south=None, east=None, west=None,
footprint_type='building', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000):
"""
Download OpenStreetMap footprint data.
Parameters
----------
... | python | def osm_footprints_download(polygon=None, north=None, south=None, east=None, west=None,
footprint_type='building', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000):
"""
Download OpenStreetMap footprint data.
Parameters
----------
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polygon : shapely Polygon or MultiPolygon
geographic shape to fetch the footprints within
north : float
northern latitude of bounding box
south : float
southern latitude of bounding box
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gboeing/osmnx | osmnx/footprints.py | create_footprints_gdf | def create_footprints_gdf(polygon=None, north=None, south=None, east=None, west=None,
footprint_type='building', retain_invalid=False):
"""
Get footprint data from OSM then assemble it into a GeoDataFrame.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
... | python | def create_footprints_gdf(polygon=None, north=None, south=None, east=None, west=None,
footprint_type='building', retain_invalid=False):
"""
Get footprint data from OSM then assemble it into a GeoDataFrame.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
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gboeing/osmnx | osmnx/footprints.py | footprints_from_point | def footprints_from_point(point, distance, footprint_type='building', retain_invalid=False):
"""
Get footprints within some distance north, south, east, and west of
a lat-long point.
Parameters
----------
point : tuple
a lat-long point
distance : numeric
distance in meters
... | python | def footprints_from_point(point, distance, footprint_type='building', retain_invalid=False):
"""
Get footprints within some distance north, south, east, and west of
a lat-long point.
Parameters
----------
point : tuple
a lat-long point
distance : numeric
distance in meters
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gboeing/osmnx | osmnx/footprints.py | footprints_from_address | def footprints_from_address(address, distance, footprint_type='building', retain_invalid=False):
"""
Get footprints within some distance north, south, east, and west of
an address.
Parameters
----------
address : string
the address to geocode to a lat-long point
distance : numeric
... | python | def footprints_from_address(address, distance, footprint_type='building', retain_invalid=False):
"""
Get footprints within some distance north, south, east, and west of
an address.
Parameters
----------
address : string
the address to geocode to a lat-long point
distance : numeric
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gboeing/osmnx | osmnx/footprints.py | footprints_from_polygon | def footprints_from_polygon(polygon, footprint_type='building', retain_invalid=False):
"""
Get footprints within some polygon.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
the shape to get data within. coordinates should be in units of
latitude-longitude degrees.
... | python | def footprints_from_polygon(polygon, footprint_type='building', retain_invalid=False):
"""
Get footprints within some polygon.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
the shape to get data within. coordinates should be in units of
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gboeing/osmnx | osmnx/footprints.py | footprints_from_place | def footprints_from_place(place, footprint_type='building', retain_invalid=False):
"""
Get footprints within the boundaries of some place.
The query must be geocodable and OSM must have polygon boundaries for the
geocode result. If OSM does not have a polygon for this place, you can
instead get its... | python | def footprints_from_place(place, footprint_type='building', retain_invalid=False):
"""
Get footprints within the boundaries of some place.
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gboeing/osmnx | osmnx/footprints.py | plot_footprints | def plot_footprints(gdf, fig=None, ax=None, figsize=None, color='#333333', bgcolor='w',
set_bounds=True, bbox=None, save=False, show=True, close=False,
filename='image', file_format='png', dpi=600):
"""
Plot a GeoDataFrame of footprints.
Parameters
----------
... | python | def plot_footprints(gdf, fig=None, ax=None, figsize=None, color='#333333', bgcolor='w',
set_bounds=True, bbox=None, save=False, show=True, close=False,
filename='image', file_format='png', dpi=600):
"""
Plot a GeoDataFrame of footprints.
Parameters
----------
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gboeing/osmnx | osmnx/elevation.py | add_node_elevations | def add_node_elevations(G, api_key, max_locations_per_batch=350,
pause_duration=0.02): # pragma: no cover
"""
Get the elevation (meters) of each node in the network and add it to the
node as an attribute.
Parameters
----------
G : networkx multidigraph
api_key : stri... | python | def add_node_elevations(G, api_key, max_locations_per_batch=350,
pause_duration=0.02): # pragma: no cover
"""
Get the elevation (meters) of each node in the network and add it to the
node as an attribute.
Parameters
----------
G : networkx multidigraph
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Get the directed grade (ie, rise over run) for each edge in the network and
add it to the edge as an attribute. Nodes must have elevation attributes to
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Parameters
----------
G : networkx multidigraph
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"""
Get the directed grade (ie, rise over run) for each edge in the network and
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G : networkx multidigraph
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gboeing/osmnx | osmnx/save_load.py | save_gdf_shapefile | def save_gdf_shapefile(gdf, filename=None, folder=None):
"""
Save a GeoDataFrame of place shapes or footprints as an ESRI
shapefile.
Parameters
----------
gdf : GeoDataFrame
the gdf to be saved
filename : string
what to call the shapefile (file extensions are added automatic... | python | def save_gdf_shapefile(gdf, filename=None, folder=None):
"""
Save a GeoDataFrame of place shapes or footprints as an ESRI
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Parameters
----------
gdf : GeoDataFrame
the gdf to be saved
filename : string
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shapefile. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
gboeing/osmnx | osmnx/save_load.py | save_graph_shapefile | def save_graph_shapefile(G, filename='graph', folder=None, encoding='utf-8'):
"""
Save graph nodes and edges as ESRI shapefiles to disk.
Parameters
----------
G : networkx multidigraph
filename : string
the name of the shapefiles (not including file extensions)
folder : string
... | python | def save_graph_shapefile(G, filename='graph', folder=None, encoding='utf-8'):
"""
Save graph nodes and edges as ESRI shapefiles to disk.
Parameters
----------
G : networkx multidigraph
filename : string
the name of the shapefiles (not including file extensions)
folder : string
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gboeing/osmnx | osmnx/save_load.py | save_graph_osm | def save_graph_osm(G, node_tags=settings.osm_xml_node_tags,
node_attrs=settings.osm_xml_node_attrs,
edge_tags=settings.osm_xml_way_tags,
edge_attrs=settings.osm_xml_way_attrs,
oneway=True, filename='graph.osm',
folder=None):
... | python | def save_graph_osm(G, node_tags=settings.osm_xml_node_tags,
node_attrs=settings.osm_xml_node_attrs,
edge_tags=settings.osm_xml_way_tags,
edge_attrs=settings.osm_xml_way_attrs,
oneway=True, filename='graph.osm',
folder=None):
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gboeing/osmnx | osmnx/save_load.py | save_graphml | def save_graphml(G, filename='graph.graphml', folder=None, gephi=False):
"""
Save graph as GraphML file to disk.
Parameters
----------
G : networkx multidigraph
filename : string
the name of the graphml file (including file extension)
folder : string
the folder to contain th... | python | def save_graphml(G, filename='graph.graphml', folder=None, gephi=False):
"""
Save graph as GraphML file to disk.
Parameters
----------
G : networkx multidigraph
filename : string
the name of the graphml file (including file extension)
folder : string
the folder to contain th... | [
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gboeing/osmnx | osmnx/save_load.py | load_graphml | def load_graphml(filename, folder=None, node_type=int):
"""
Load a GraphML file from disk and convert the node/edge attributes to
correct data types.
Parameters
----------
filename : string
the name of the graphml file (including file extension)
folder : string
the folder co... | python | def load_graphml(filename, folder=None, node_type=int):
"""
Load a GraphML file from disk and convert the node/edge attributes to
correct data types.
Parameters
----------
filename : string
the name of the graphml file (including file extension)
folder : string
the folder co... | [
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gboeing/osmnx | osmnx/save_load.py | is_duplicate_edge | def is_duplicate_edge(data, data_other):
"""
Check if two edge data dictionaries are the same based on OSM ID and
geometry.
Parameters
----------
data : dict
the first edge's data
data_other : dict
the second edge's data
Returns
-------
is_dupe : bool
"""
... | python | def is_duplicate_edge(data, data_other):
"""
Check if two edge data dictionaries are the same based on OSM ID and
geometry.
Parameters
----------
data : dict
the first edge's data
data_other : dict
the second edge's data
Returns
-------
is_dupe : bool
"""
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gboeing/osmnx | osmnx/save_load.py | is_same_geometry | def is_same_geometry(ls1, ls2):
"""
Check if LineString geometries in two edges are the same, in
normal or reversed order of points.
Parameters
----------
ls1 : LineString
the first edge's geometry
ls2 : LineString
the second edge's geometry
Returns
-------
bool... | python | def is_same_geometry(ls1, ls2):
"""
Check if LineString geometries in two edges are the same, in
normal or reversed order of points.
Parameters
----------
ls1 : LineString
the first edge's geometry
ls2 : LineString
the second edge's geometry
Returns
-------
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gboeing/osmnx | osmnx/save_load.py | update_edge_keys | def update_edge_keys(G):
"""
Update the keys of edges that share a u, v with another edge but differ in
geometry. For example, two one-way streets from u to v that bow away from
each other as separate streets, rather than opposite direction edges of a
single street.
Parameters
--------... | python | def update_edge_keys(G):
"""
Update the keys of edges that share a u, v with another edge but differ in
geometry. For example, two one-way streets from u to v that bow away from
each other as separate streets, rather than opposite direction edges of a
single street.
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gboeing/osmnx | osmnx/save_load.py | get_undirected | def get_undirected(G):
"""
Convert a directed graph to an undirected graph that maintains parallel
edges if geometries differ.
Parameters
----------
G : networkx multidigraph
Returns
-------
networkx multigraph
"""
start_time = time.time()
# set from/to nodes before m... | python | def get_undirected(G):
"""
Convert a directed graph to an undirected graph that maintains parallel
edges if geometries differ.
Parameters
----------
G : networkx multidigraph
Returns
-------
networkx multigraph
"""
start_time = time.time()
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gboeing/osmnx | osmnx/save_load.py | graph_to_gdfs | def graph_to_gdfs(G, nodes=True, edges=True, node_geometry=True, fill_edge_geometry=True):
"""
Convert a graph into node and/or edge GeoDataFrames
Parameters
----------
G : networkx multidigraph
nodes : bool
if True, convert graph nodes to a GeoDataFrame and return it
edges : bool
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"""
Convert a graph into node and/or edge GeoDataFrames
Parameters
----------
G : networkx multidigraph
nodes : bool
if True, convert graph nodes to a GeoDataFrame and return it
edges : bool
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gboeing/osmnx | osmnx/save_load.py | gdfs_to_graph | def gdfs_to_graph(gdf_nodes, gdf_edges):
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Convert node and edge GeoDataFrames into a graph
Parameters
----------
gdf_nodes : GeoDataFrame
gdf_edges : GeoDataFrame
Returns
-------
networkx multidigraph
"""
G = nx.MultiDiGraph()
G.graph['crs'] = gdf_nodes.crs
G.gr... | python | def gdfs_to_graph(gdf_nodes, gdf_edges):
"""
Convert node and edge GeoDataFrames into a graph
Parameters
----------
gdf_nodes : GeoDataFrame
gdf_edges : GeoDataFrame
Returns
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networkx multidigraph
"""
G = nx.MultiDiGraph()
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Create a filename string in a consistent format from a place name string.
Parameters
----------
place_name : string
place name to convert into a filename
Returns
-------
string
"""
name_pieces = list(reversed(place_name.split(', ')... | python | def make_shp_filename(place_name):
"""
Create a filename string in a consistent format from a place name string.
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place_name : string
place name to convert into a filename
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string
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"""
Calculate basic descriptive metric and topological stats for a graph.
For an unprojected lat-lng graph, tolerance and graph units should be in
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"""
Calculate basic descriptive metric and topological stats for a graph.
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"""
Calculate extended topological stats and metrics for a graph.
Many of these algorithms have an inherently high time complexity. Global
topological analysis of large complex networks is extremely time consuming
... | python | def extended_stats(G, connectivity=False, anc=False, ecc=False, bc=False, cc=False):
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Calculate extended topological stats and metrics for a graph.
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | backward_induction | def backward_induction(ddp, T, v_term=None):
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Solve by backward induction a :math:`T`-period finite horizon
discrete dynamic program with stationary reward and transition
probability functions :math:`r` and :math:`q` and discount factor
:math:`\beta \in [0, 1]`.
The optimal value functions ... | python | def backward_induction(ddp, T, v_term=None):
r"""
Solve by backward induction a :math:`T`-period finite horizon
discrete dynamic program with stationary reward and transition
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Check whether `s_indices` and `a_indices` are sorted in
lexicographic order.
Parameters
----------
s_indices, a_indices : ndarray(ndim=1)
Returns
-------
bool
Whether `s_indices` and `a_indices` are sorted.
"""
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"""
Check whether `s_indices` and `a_indices` are sorted in
lexicographic order.
Parameters
----------
s_indices, a_indices : ndarray(ndim=1)
Returns
-------
bool
Whether `s_indices` and `a_indices` are sorted.
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | _generate_a_indptr | def _generate_a_indptr(num_states, s_indices, out):
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Parameters
----------
num_states : scalar(int)
s_indices : ndarray(int, ndim=1)
out : ndarray(int, ndim=1)
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP._check_action_feasibility | def _check_action_feasibility(self):
"""
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"""
# Check that for every state, reward is finite for some action
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"""
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.to_sa_pair_form | def to_sa_pair_form(self, sparse=True):
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Convert this instance of `DiscreteDP` to SA-pair form
Parameters
----------
sparse : bool, optional(default=True)
Should the `Q` matrix be stored as a sparse matrix?
If true the CSR format is used
Retur... | python | def to_sa_pair_form(self, sparse=True):
"""
Convert this instance of `DiscreteDP` to SA-pair form
Parameters
----------
sparse : bool, optional(default=True)
Should the `Q` matrix be stored as a sparse matrix?
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.to_product_form | def to_product_form(self):
"""
Convert this instance of `DiscreteDP` to the "product" form.
The product form uses the version of the init method taking
`R`, `Q` and `beta`.
Parameters
----------
Returns
-------
ddp_sa : DiscreteDP
Th... | python | def to_product_form(self):
"""
Convert this instance of `DiscreteDP` to the "product" form.
The product form uses the version of the init method taking
`R`, `Q` and `beta`.
Parameters
----------
Returns
-------
ddp_sa : DiscreteDP
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.RQ_sigma | def RQ_sigma(self, sigma):
"""
Given a policy `sigma`, return the reward vector `R_sigma` and
the transition probability matrix `Q_sigma`.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
Returns
-------
... | python | def RQ_sigma(self, sigma):
"""
Given a policy `sigma`, return the reward vector `R_sigma` and
the transition probability matrix `Q_sigma`.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
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"""
The Bellman operator, which computes and returns the updated
value function `Tv` for a value function `v`.
Parameters
----------
v : array_like(float, ndim=1)
Value function vector, of length n.
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"""
The Bellman operator, which computes and returns the updated
value function `Tv` for a value function `v`.
Parameters
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v : array_like(float, ndim=1)
Value function vector, of length n.
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.T_sigma | def T_sigma(self, sigma):
"""
Given a policy `sigma`, return the T_sigma operator.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
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The T_sigma operator.
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"""
Given a policy `sigma`, return the T_sigma operator.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
Returns
-------
callable
The T_sigma operator.
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.compute_greedy | def compute_greedy(self, v, sigma=None):
"""
Compute the v-greedy policy.
Parameters
----------
v : array_like(float, ndim=1)
Value function vector, of length n.
sigma : ndarray(int, ndim=1), optional(default=None)
Optional output array for `sigm... | python | def compute_greedy(self, v, sigma=None):
"""
Compute the v-greedy policy.
Parameters
----------
v : array_like(float, ndim=1)
Value function vector, of length n.
sigma : ndarray(int, ndim=1), optional(default=None)
Optional output array for `sigm... | [
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.evaluate_policy | def evaluate_policy(self, sigma):
"""
Compute the value of a policy.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
Returns
-------
v_sigma : ndarray(float, ndim=1)
Value vector of `sigma`, of le... | python | def evaluate_policy(self, sigma):
"""
Compute the value of a policy.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
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-------
v_sigma : ndarray(float, ndim=1)
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.operator_iteration | def operator_iteration(self, T, v, max_iter, tol=None, *args, **kwargs):
"""
Iteratively apply the operator `T` to `v`. Modify `v` in-place.
Iteration is performed for at most a number `max_iter` of times.
If `tol` is specified, it is terminated once the distance of
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"""
Iteratively apply the operator `T` to `v`. Modify `v` in-place.
Iteration is performed for at most a number `max_iter` of times.
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.solve | def solve(self, method='policy_iteration',
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"""
Solve the dynamic programming problem.
Parameters
----------
method : str, optinal(default='policy_iteration')
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v_init=None, epsilon=None, max_iter=None, k=20):
"""
Solve the dynamic programming problem.
Parameters
----------
method : str, optinal(default='policy_iteration')
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.value_iteration | def value_iteration(self, v_init=None, epsilon=None, max_iter=None):
"""
Solve the optimization problem by value iteration. See the
`solve` method.
"""
if self.beta == 1:
raise NotImplementedError(self._error_msg_no_discounting)
if max_iter is None:
... | python | def value_iteration(self, v_init=None, epsilon=None, max_iter=None):
"""
Solve the optimization problem by value iteration. See the
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"""
if self.beta == 1:
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.policy_iteration | def policy_iteration(self, v_init=None, max_iter=None):
"""
Solve the optimization problem by policy iteration. See the
`solve` method.
"""
if self.beta == 1:
raise NotImplementedError(self._error_msg_no_discounting)
if max_iter is None:
max_iter... | python | def policy_iteration(self, v_init=None, max_iter=None):
"""
Solve the optimization problem by policy iteration. See the
`solve` method.
"""
if self.beta == 1:
raise NotImplementedError(self._error_msg_no_discounting)
if max_iter is None:
max_iter... | [
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.modified_policy_iteration | def modified_policy_iteration(self, v_init=None, epsilon=None,
max_iter=None, k=20):
"""
Solve the optimization problem by modified policy iteration. See
the `solve` method.
"""
if self.beta == 1:
raise NotImplementedError(self._erro... | python | def modified_policy_iteration(self, v_init=None, epsilon=None,
max_iter=None, k=20):
"""
Solve the optimization problem by modified policy iteration. See
the `solve` method.
"""
if self.beta == 1:
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QuantEcon/QuantEcon.py | quantecon/markov/ddp.py | DiscreteDP.controlled_mc | def controlled_mc(self, sigma):
"""
Returns the controlled Markov chain for a given policy `sigma`.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
Returns
-------
mc : MarkovChain
Controlled Mark... | python | def controlled_mc(self, sigma):
"""
Returns the controlled Markov chain for a given policy `sigma`.
Parameters
----------
sigma : array_like(int, ndim=1)
Policy vector, of length n.
Returns
-------
mc : MarkovChain
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QuantEcon/QuantEcon.py | quantecon/estspec.py | smooth | def smooth(x, window_len=7, window='hanning'):
"""
Smooth the data in x using convolution with a window of requested
size and type.
Parameters
----------
x : array_like(float)
A flat NumPy array containing the data to smooth
window_len : scalar(int), optional
An odd integer ... | python | def smooth(x, window_len=7, window='hanning'):
"""
Smooth the data in x using convolution with a window of requested
size and type.
Parameters
----------
x : array_like(float)
A flat NumPy array containing the data to smooth
window_len : scalar(int), optional
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QuantEcon/QuantEcon.py | quantecon/estspec.py | periodogram | def periodogram(x, window=None, window_len=7):
r"""
Computes the periodogram
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r"""
Computes the periodogram
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I(w) = \frac{1}{n} \Big[ \sum_{t=0}^{n-1} x_t e^{itw} \Big] ^2
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QuantEcon/QuantEcon.py | quantecon/estspec.py | ar_periodogram | def ar_periodogram(x, window='hanning', window_len=7):
"""
Compute periodogram from data x, using prewhitening, smoothing and
recoloring. The data is fitted to an AR(1) model for prewhitening,
and the residuals are used to compute a first-pass periodogram with
smoothing. The fitted coefficients ar... | python | def ar_periodogram(x, window='hanning', window_len=7):
"""
Compute periodogram from data x, using prewhitening, smoothing and
recoloring. The data is fitted to an AR(1) model for prewhitening,
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QuantEcon/QuantEcon.py | quantecon/game_theory/support_enumeration.py | support_enumeration_gen | def support_enumeration_gen(g):
"""
Generator version of `support_enumeration`.
Parameters
----------
g : NormalFormGame
NormalFormGame instance with 2 players.
Yields
-------
tuple(ndarray(float, ndim=1))
Tuple of Nash equilibrium mixed actions.
"""
try:
... | python | def support_enumeration_gen(g):
"""
Generator version of `support_enumeration`.
Parameters
----------
g : NormalFormGame
NormalFormGame instance with 2 players.
Yields
-------
tuple(ndarray(float, ndim=1))
Tuple of Nash equilibrium mixed actions.
"""
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QuantEcon/QuantEcon.py | quantecon/game_theory/support_enumeration.py | _support_enumeration_gen | def _support_enumeration_gen(payoff_matrices):
"""
Main body of `support_enumeration_gen`.
Parameters
----------
payoff_matrices : tuple(ndarray(float, ndim=2))
Tuple of payoff matrices, of shapes (m, n) and (n, m),
respectively.
Yields
------
out : tuple(ndarray(float,... | python | def _support_enumeration_gen(payoff_matrices):
"""
Main body of `support_enumeration_gen`.
Parameters
----------
payoff_matrices : tuple(ndarray(float, ndim=2))
Tuple of payoff matrices, of shapes (m, n) and (n, m),
respectively.
Yields
------
out : tuple(ndarray(float,... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/support_enumeration.py | _indiff_mixed_action | def _indiff_mixed_action(payoff_matrix, own_supp, opp_supp, A, out):
"""
Given a player's payoff matrix `payoff_matrix`, an array `own_supp`
of this player's actions, and an array `opp_supp` of the opponent's
actions, each of length k, compute the opponent's mixed action whose
support equals `opp_su... | python | def _indiff_mixed_action(payoff_matrix, own_supp, opp_supp, A, out):
"""
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | pure2mixed | def pure2mixed(num_actions, action):
"""
Convert a pure action to the corresponding mixed action.
Parameters
----------
num_actions : scalar(int)
The number of the pure actions (= the length of a mixed action).
action : scalar(int)
The pure action to convert to the correspondin... | python | def pure2mixed(num_actions, action):
"""
Convert a pure action to the corresponding mixed action.
Parameters
----------
num_actions : scalar(int)
The number of the pure actions (= the length of a mixed action).
action : scalar(int)
The pure action to convert to the correspondin... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | best_response_2p | def best_response_2p(payoff_matrix, opponent_mixed_action, tol=1e-8):
"""
Numba-optimized version of `Player.best_response` compilied in
nopython mode, specialized for 2-player games (where there is only
one opponent).
Return the best response action (with the smallest index if more
than one) t... | python | def best_response_2p(payoff_matrix, opponent_mixed_action, tol=1e-8):
"""
Numba-optimized version of `Player.best_response` compilied in
nopython mode, specialized for 2-player games (where there is only
one opponent).
Return the best response action (with the smallest index if more
than one) t... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.delete_action | def delete_action(self, action, player_idx=0):
"""
Return a new `Player` instance with the action(s) specified by
`action` deleted from the action set of the player specified by
`player_idx`. Deletion is not performed in place.
Parameters
----------
action : scal... | python | def delete_action(self, action, player_idx=0):
"""
Return a new `Player` instance with the action(s) specified by
`action` deleted from the action set of the player specified by
`player_idx`. Deletion is not performed in place.
Parameters
----------
action : scal... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.payoff_vector | def payoff_vector(self, opponents_actions):
"""
Return an array of payoff values, one for each own action, given
a profile of the opponents' actions.
Parameters
----------
opponents_actions : see `best_response`.
Returns
-------
payoff_vector : n... | python | def payoff_vector(self, opponents_actions):
"""
Return an array of payoff values, one for each own action, given
a profile of the opponents' actions.
Parameters
----------
opponents_actions : see `best_response`.
Returns
-------
payoff_vector : n... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.is_best_response | def is_best_response(self, own_action, opponents_actions, tol=None):
"""
Return True if `own_action` is a best response to
`opponents_actions`.
Parameters
----------
own_action : scalar(int) or array_like(float, ndim=1)
An integer representing a pure action, ... | python | def is_best_response(self, own_action, opponents_actions, tol=None):
"""
Return True if `own_action` is a best response to
`opponents_actions`.
Parameters
----------
own_action : scalar(int) or array_like(float, ndim=1)
An integer representing a pure action, ... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.best_response | def best_response(self, opponents_actions, tie_breaking='smallest',
payoff_perturbation=None, tol=None, random_state=None):
"""
Return the best response action(s) to `opponents_actions`.
Parameters
----------
opponents_actions : scalar(int) or array_like
... | python | def best_response(self, opponents_actions, tie_breaking='smallest',
payoff_perturbation=None, tol=None, random_state=None):
"""
Return the best response action(s) to `opponents_actions`.
Parameters
----------
opponents_actions : scalar(int) or array_like
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.random_choice | def random_choice(self, actions=None, random_state=None):
"""
Return a pure action chosen randomly from `actions`.
Parameters
----------
actions : array_like(int), optional(default=None)
An array of integers representing pure actions.
random_state : int or n... | python | def random_choice(self, actions=None, random_state=None):
"""
Return a pure action chosen randomly from `actions`.
Parameters
----------
actions : array_like(int), optional(default=None)
An array of integers representing pure actions.
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.is_dominated | def is_dominated(self, action, tol=None, method=None):
"""
Determine whether `action` is strictly dominated by some mixed
action.
Parameters
----------
action : scalar(int)
Integer representing a pure action.
tol : scalar(float), optional(default=Non... | python | def is_dominated(self, action, tol=None, method=None):
"""
Determine whether `action` is strictly dominated by some mixed
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----------
action : scalar(int)
Integer representing a pure action.
tol : scalar(float), optional(default=Non... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | Player.dominated_actions | def dominated_actions(self, tol=None, method=None):
"""
Return a list of actions that are strictly dominated by some
mixed actions.
Parameters
----------
tol : scalar(float), optional(default=None)
Tolerance level used in determining domination. If None,
... | python | def dominated_actions(self, tol=None, method=None):
"""
Return a list of actions that are strictly dominated by some
mixed actions.
Parameters
----------
tol : scalar(float), optional(default=None)
Tolerance level used in determining domination. If None,
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | NormalFormGame.delete_action | def delete_action(self, player_idx, action):
"""
Return a new `NormalFormGame` instance with the action(s)
specified by `action` deleted from the action set of the player
specified by `player_idx`. Deletion is not performed in place.
Parameters
----------
player_... | python | def delete_action(self, player_idx, action):
"""
Return a new `NormalFormGame` instance with the action(s)
specified by `action` deleted from the action set of the player
specified by `player_idx`. Deletion is not performed in place.
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QuantEcon/QuantEcon.py | quantecon/game_theory/normal_form_game.py | NormalFormGame.is_nash | def is_nash(self, action_profile, tol=None):
"""
Return True if `action_profile` is a Nash equilibrium.
Parameters
----------
action_profile : array_like(int or array_like(float))
An array of N objects, where each object must be an integer
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"""
Return True if `action_profile` is a Nash equilibrium.
Parameters
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action_profile : array_like(int or array_like(float))
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QuantEcon/QuantEcon.py | quantecon/game_theory/mclennan_tourky.py | mclennan_tourky | def mclennan_tourky(g, init=None, epsilon=1e-3, max_iter=200,
full_output=False):
r"""
Find one mixed-action epsilon-Nash equilibrium of an N-player normal
form game by the fixed point computation algorithm by McLennan and
Tourky [1]_.
Parameters
----------
g : NormalFor... | python | def mclennan_tourky(g, init=None, epsilon=1e-3, max_iter=200,
full_output=False):
r"""
Find one mixed-action epsilon-Nash equilibrium of an N-player normal
form game by the fixed point computation algorithm by McLennan and
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QuantEcon/QuantEcon.py | quantecon/game_theory/mclennan_tourky.py | _best_response_selection | def _best_response_selection(x, g, indptr=None):
"""
Selection of the best response correspondence of `g` that selects
the best response action with the smallest index when there are
ties, where the input and output are flattened action profiles.
Parameters
----------
x : array_like(float, ... | python | def _best_response_selection(x, g, indptr=None):
"""
Selection of the best response correspondence of `g` that selects
the best response action with the smallest index when there are
ties, where the input and output are flattened action profiles.
Parameters
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QuantEcon/QuantEcon.py | quantecon/game_theory/mclennan_tourky.py | _is_epsilon_nash | def _is_epsilon_nash(x, g, epsilon, indptr=None):
"""
Determine whether `x` is an `epsilon`-Nash equilibrium of `g`.
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----------
x : array_like(float, ndim=1)
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"""
Determine whether `x` is an `epsilon`-Nash equilibrium of `g`.
Parameters
----------
x : array_like(float, ndim=1)
Array of flattened mixed action profile of length equal to n_0 +
... + n_N-1, where `out[indptr[i]:indptr[i+1]]` c... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/mclennan_tourky.py | _get_action_profile | def _get_action_profile(x, indptr):
"""
Obtain a tuple of mixed actions from a flattened action profile.
Parameters
----------
x : array_like(float, ndim=1)
Array of flattened mixed action profile of length equal to n_0 +
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"""
Obtain a tuple of mixed actions from a flattened action profile.
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----------
x : array_like(float, ndim=1)
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"""
Flatten the given action profile.
Parameters
----------
action_profile : array_like(int or array_like(float, ndim=1))
Profile of actions of the N players, where each player i' action
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"""
Flatten the given action profile.
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action_profile : array_like(int or array_like(float, ndim=1))
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | blotto_game | def blotto_game(h, t, rho, mu=0, random_state=None):
"""
Return a NormalFormGame instance of a 2-player non-zero sum Colonel
Blotto game (Hortala-Vallve and Llorente-Saguer, 2012), where the
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(so that the number of actions for each pla... | python | def blotto_game(h, t, rho, mu=0, random_state=None):
"""
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | _populate_blotto_payoff_arrays | def _populate_blotto_payoff_arrays(payoff_arrays, actions, values):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
the Blotto game with h hills and t troops.
Parameters
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payoff_arrays : tuple(ndarray(float, ndim=2))
Tuple of 2 ndarrays of shape (n, n), ... | python | def _populate_blotto_payoff_arrays(payoff_arrays, actions, values):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
the Blotto game with h hills and t troops.
Parameters
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payoff_arrays : tuple(ndarray(float, ndim=2))
Tuple of 2 ndarrays of shape (n, n), ... | [
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | ranking_game | def ranking_game(n, steps=10, random_state=None):
"""
Return a NormalFormGame instance of (the 2-player version of) the
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"""
Return a NormalFormGame instance of (the 2-player version of) the
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | _populate_ranking_payoff_arrays | def _populate_ranking_payoff_arrays(payoff_arrays, scores, costs):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
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Parameters
----------
payoff_arrays : tuple(ndarray(float, ndim=2))
Tuple of 2 ndarrays of shape (n, n).... | python | def _populate_ranking_payoff_arrays(payoff_arrays, scores, costs):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
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----------
payoff_arrays : tuple(ndarray(float, ndim=2))
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | sgc_game | def sgc_game(k):
"""
Return a NormalFormGame instance of the 2-player game introduced by
Sandholm, Gilpin, and Conitzer (2005), which has a unique Nash
equilibrium, where each player plays half of the actions with
positive probabilities. Payoffs are normalized so that the minimum
and the maximum... | python | def sgc_game(k):
"""
Return a NormalFormGame instance of the 2-player game introduced by
Sandholm, Gilpin, and Conitzer (2005), which has a unique Nash
equilibrium, where each player plays half of the actions with
positive probabilities. Payoffs are normalized so that the minimum
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | _populate_sgc_payoff_arrays | def _populate_sgc_payoff_arrays(payoff_arrays):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
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Parameters
----------
payoff_arrays : tuple(ndarray(float, ndim=2))
Tuple of 2 ndarrays of shape (4*k-1, 4*k-1). Modified in place.
"""
n = payof... | python | def _populate_sgc_payoff_arrays(payoff_arrays):
"""
Populate the ndarrays in `payoff_arrays` with the payoff values of
the SGC game.
Parameters
----------
payoff_arrays : tuple(ndarray(float, ndim=2))
Tuple of 2 ndarrays of shape (4*k-1, 4*k-1). Modified in place.
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | tournament_game | def tournament_game(n, k, random_state=None):
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | _populate_tournament_payoff_array0 | def _populate_tournament_payoff_array0(payoff_array, k, indices, indptr):
"""
Populate `payoff_array` with the payoff values for player 0 in the
tournament game given a random tournament graph in CSR format.
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payoff_array : ndarray(float, ndim=2)
ndarray of shape (n... | python | def _populate_tournament_payoff_array0(payoff_array, k, indices, indptr):
"""
Populate `payoff_array` with the payoff values for player 0 in the
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | _populate_tournament_payoff_array1 | def _populate_tournament_payoff_array1(payoff_array, k):
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Populate `payoff_array` with the payoff values for player 1 in the
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payoff_array : ndarray(float, ndim=2)
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"""
Populate `payoff_array` with the payoff values for player 1 in the
tournament game.
Parameters
----------
payoff_array : ndarray(float, ndim=2)
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QuantEcon/QuantEcon.py | quantecon/game_theory/game_generators/bimatrix_generators.py | unit_vector_game | def unit_vector_game(n, avoid_pure_nash=False, random_state=None):
"""
Return a NormalFormGame instance of the 2-player game "unit vector
game" (Savani and von Stengel, 2016). Payoffs for player 1 are
chosen randomly from the [0, 1) range. For player 0, each column
contains exactly one 1 payoff and ... | python | def unit_vector_game(n, avoid_pure_nash=False, random_state=None):
"""
Return a NormalFormGame instance of the 2-player game "unit vector
game" (Savani and von Stengel, 2016). Payoffs for player 1 are
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QuantEcon/QuantEcon.py | quantecon/markov/gth_solve.py | gth_solve | def gth_solve(A, overwrite=False, use_jit=True):
r"""
This routine computes the stationary distribution of an irreducible
Markov transition matrix (stochastic matrix) or transition rate
matrix (generator matrix) `A`.
More generally, given a Metzler matrix (square matrix whose
off-diagonal entri... | python | def gth_solve(A, overwrite=False, use_jit=True):
r"""
This routine computes the stationary distribution of an irreducible
Markov transition matrix (stochastic matrix) or transition rate
matrix (generator matrix) `A`.
More generally, given a Metzler matrix (square matrix whose
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QuantEcon/QuantEcon.py | quantecon/markov/gth_solve.py | _gth_solve_jit | def _gth_solve_jit(A, out):
"""
JIT complied version of the main routine of gth_solve.
Parameters
----------
A : numpy.ndarray(float, ndim=2)
Stochastic matrix or generator matrix. Must be of shape n x n.
Data will be overwritten.
out : numpy.ndarray(float, ndim=1)
Outp... | python | def _gth_solve_jit(A, out):
"""
JIT complied version of the main routine of gth_solve.
Parameters
----------
A : numpy.ndarray(float, ndim=2)
Stochastic matrix or generator matrix. Must be of shape n x n.
Data will be overwritten.
out : numpy.ndarray(float, ndim=1)
Outp... | [
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | _csr_matrix_indices | def _csr_matrix_indices(S):
"""
Generate the indices of nonzero entries of a csr_matrix S
"""
m, n = S.shape
for i in range(m):
for j in range(S.indptr[i], S.indptr[i+1]):
row_index, col_index = i, S.indices[j]
yield row_index, col_index | python | def _csr_matrix_indices(S):
"""
Generate the indices of nonzero entries of a csr_matrix S
"""
m, n = S.shape
for i in range(m):
for j in range(S.indptr[i], S.indptr[i+1]):
row_index, col_index = i, S.indices[j]
yield row_index, col_index | [
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | random_tournament_graph | def random_tournament_graph(n, random_state=None):
"""
Return a random tournament graph [1]_ with n nodes.
Parameters
----------
n : scalar(int)
Number of nodes.
random_state : int or np.random.RandomState, optional
Random seed (integer) or np.random.RandomState instance to set... | python | def random_tournament_graph(n, random_state=None):
"""
Return a random tournament graph [1]_ with n nodes.
Parameters
----------
n : scalar(int)
Number of nodes.
random_state : int or np.random.RandomState, optional
Random seed (integer) or np.random.RandomState instance to set... | [
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | _populate_random_tournament_row_col | def _populate_random_tournament_row_col(n, r, row, col):
"""
Populate ndarrays `row` and `col` with directed edge indices
determined by random numbers in `r` for a tournament graph with n
nodes, which has num_edges = n * (n-1) // 2 edges.
Parameters
----------
n : scalar(int)
Number... | python | def _populate_random_tournament_row_col(n, r, row, col):
"""
Populate ndarrays `row` and `col` with directed edge indices
determined by random numbers in `r` for a tournament graph with n
nodes, which has num_edges = n * (n-1) // 2 edges.
Parameters
----------
n : scalar(int)
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | DiGraph._find_scc | def _find_scc(self):
"""
Set ``self._num_scc`` and ``self._scc_proj``
by calling ``scipy.sparse.csgraph.connected_components``:
* docs.scipy.org/doc/scipy/reference/sparse.csgraph.html
* github.com/scipy/scipy/blob/master/scipy/sparse/csgraph/_traversal.pyx
``self._scc_p... | python | def _find_scc(self):
"""
Set ``self._num_scc`` and ``self._scc_proj``
by calling ``scipy.sparse.csgraph.connected_components``:
* docs.scipy.org/doc/scipy/reference/sparse.csgraph.html
* github.com/scipy/scipy/blob/master/scipy/sparse/csgraph/_traversal.pyx
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | DiGraph._condensation_lil | def _condensation_lil(self):
"""
Return the sparse matrix representation of the condensation digraph
in lil format.
"""
condensation_lil = sparse.lil_matrix(
(self.num_strongly_connected_components,
self.num_strongly_connected_components), dtype=bool
... | python | def _condensation_lil(self):
"""
Return the sparse matrix representation of the condensation digraph
in lil format.
"""
condensation_lil = sparse.lil_matrix(
(self.num_strongly_connected_components,
self.num_strongly_connected_components), dtype=bool
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | DiGraph._find_sink_scc | def _find_sink_scc(self):
"""
Set self._sink_scc_labels, which is a list containing the labels of
the strongly connected components.
"""
condensation_lil = self._condensation_lil()
# A sink SCC is a SCC such that none of its members is strongly
# connected to no... | python | def _find_sink_scc(self):
"""
Set self._sink_scc_labels, which is a list containing the labels of
the strongly connected components.
"""
condensation_lil = self._condensation_lil()
# A sink SCC is a SCC such that none of its members is strongly
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | DiGraph._compute_period | def _compute_period(self):
"""
Set ``self._period`` and ``self._cyclic_components_proj``.
Use the algorithm described in:
J. P. Jarvis and D. R. Shier,
"Graph-Theoretic Analysis of Finite Markov Chains," 1996.
"""
# Degenerate graph with a single node (which is ... | python | def _compute_period(self):
"""
Set ``self._period`` and ``self._cyclic_components_proj``.
Use the algorithm described in:
J. P. Jarvis and D. R. Shier,
"Graph-Theoretic Analysis of Finite Markov Chains," 1996.
"""
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QuantEcon/QuantEcon.py | quantecon/graph_tools.py | DiGraph.subgraph | def subgraph(self, nodes):
"""
Return the subgraph consisting of the given nodes and edges
between thses nodes.
Parameters
----------
nodes : array_like(int, ndim=1)
Array of node indices.
Returns
-------
DiGraph
A DiGraph ... | python | def subgraph(self, nodes):
"""
Return the subgraph consisting of the given nodes and edges
between thses nodes.
Parameters
----------
nodes : array_like(int, ndim=1)
Array of node indices.
Returns
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DiGraph
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"""
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The algorithm used by this function is based on the singular value
decomposition of `A`.
Parameters
----------
A : array_like(float, ndim=1 or 2)
A should be at most 2-D. ... | python | def rank_est(A, atol=1e-13, rtol=0):
"""
Estimate the rank (i.e. the dimension of the nullspace) of a matrix.
The algorithm used by this function is based on the singular value
decomposition of `A`.
Parameters
----------
A : array_like(float, ndim=1 or 2)
A should be at most 2-D. ... | [
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QuantEcon/QuantEcon.py | quantecon/filter.py | hamilton_filter | def hamilton_filter(data, h, *args):
r"""
This function applies "Hamilton filter" to the data
http://econweb.ucsd.edu/~jhamilto/hp.pdf
Parameters
----------
data : arrray or dataframe
h : integer
Time horizon that we are likely to predict incorrectly.
Ori... | python | def hamilton_filter(data, h, *args):
r"""
This function applies "Hamilton filter" to the data
http://econweb.ucsd.edu/~jhamilto/hp.pdf
Parameters
----------
data : arrray or dataframe
h : integer
Time horizon that we are likely to predict incorrectly.
Ori... | [
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QuantEcon/QuantEcon.py | quantecon/markov/utilities.py | sa_indices | def sa_indices(num_states, num_actions):
"""
Generate `s_indices` and `a_indices` for `DiscreteDP`, for the case
where all the actions are feasible at every state.
Parameters
----------
num_states : scalar(int)
Number of states.
num_actions : scalar(int)
Number of actions.
... | python | def sa_indices(num_states, num_actions):
"""
Generate `s_indices` and `a_indices` for `DiscreteDP`, for the case
where all the actions are feasible at every state.
Parameters
----------
num_states : scalar(int)
Number of states.
num_actions : scalar(int)
Number of actions.
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | _generate_sample_paths | def _generate_sample_paths(P_cdfs, init_states, random_values, out):
"""
Generate num_reps sample paths of length ts_length, where num_reps =
out.shape[0] and ts_length = out.shape[1].
Parameters
----------
P_cdfs : ndarray(float, ndim=2)
Array containing as rows the CDFs of the state t... | python | def _generate_sample_paths(P_cdfs, init_states, random_values, out):
"""
Generate num_reps sample paths of length ts_length, where num_reps =
out.shape[0] and ts_length = out.shape[1].
Parameters
----------
P_cdfs : ndarray(float, ndim=2)
Array containing as rows the CDFs of the state t... | [
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | _generate_sample_paths_sparse | def _generate_sample_paths_sparse(P_cdfs1d, indices, indptr, init_states,
random_values, out):
"""
For sparse matrix.
Generate num_reps sample paths of length ts_length, where num_reps =
out.shape[0] and ts_length = out.shape[1].
Parameters
----------
P_cd... | python | def _generate_sample_paths_sparse(P_cdfs1d, indices, indptr, init_states,
random_values, out):
"""
For sparse matrix.
Generate num_reps sample paths of length ts_length, where num_reps =
out.shape[0] and ts_length = out.shape[1].
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----------
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | mc_sample_path | def mc_sample_path(P, init=0, sample_size=1000, random_state=None):
"""
Generates one sample path from the Markov chain represented by
(n x n) transition matrix P on state space S = {{0,...,n-1}}.
Parameters
----------
P : array_like(float, ndim=2)
A Markov transition matrix.
init ... | python | def mc_sample_path(P, init=0, sample_size=1000, random_state=None):
"""
Generates one sample path from the Markov chain represented by
(n x n) transition matrix P on state space S = {{0,...,n-1}}.
Parameters
----------
P : array_like(float, ndim=2)
A Markov transition matrix.
init ... | [
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | MarkovChain.get_index | def get_index(self, value):
"""
Return the index (or indices) of the given value (or values) in
`state_values`.
Parameters
----------
value
Value(s) to get the index (indices) for.
Returns
-------
idx : int or ndarray(int)
... | python | def get_index(self, value):
"""
Return the index (or indices) of the given value (or values) in
`state_values`.
Parameters
----------
value
Value(s) to get the index (indices) for.
Returns
-------
idx : int or ndarray(int)
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idx : int or ndarray(int)
Index of `value` if `value` is a single state v... | [
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | MarkovChain._get_index | def _get_index(self, value):
"""
Return the index of the given value in `state_values`.
Parameters
----------
value
Value to get the index for.
Returns
-------
idx : int
Index of `value`.
"""
error_msg = 'value {0... | python | def _get_index(self, value):
"""
Return the index of the given value in `state_values`.
Parameters
----------
value
Value to get the index for.
Returns
-------
idx : int
Index of `value`.
"""
error_msg = 'value {0... | [
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QuantEcon/QuantEcon.py | quantecon/markov/core.py | MarkovChain._compute_stationary | def _compute_stationary(self):
"""
Store the stationary distributions in self._stationary_distributions.
"""
if self.is_irreducible:
if not self.is_sparse: # Dense
stationary_dists = gth_solve(self.P).reshape(1, self.n)
else: # Sparse
... | python | def _compute_stationary(self):
"""
Store the stationary distributions in self._stationary_distributions.
"""
if self.is_irreducible:
if not self.is_sparse: # Dense
stationary_dists = gth_solve(self.P).reshape(1, self.n)
else: # Sparse
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