def main():
'''EDM package examples.
df denotes a Pandas DataFrame.
S and M denote dictionaries of Pandas DataFrames'''
dataFiles = [ "TentMap_rEDM.csv",
"TentMapNoise_rEDM.csv",
"circle.csv",
"block_3sp.csv",
"sardine_anchovy_sst.csv" ]
# Create map of module dataFiles pathnames in Files
Files = {}
for file in dataFiles:
filename = "data/" + file
if pkg_resources.resource_exists( EDM.__name__, filename ):
Files[ file ] = \
pkg_resources.resource_filename( EDM.__name__, filename )
else :
raise Exception( "examples.py: Failed to find data file " + \
file + " in EDM package" )
# Note the path argument is empty "", file path is in Files{}
df = EDM.EmbedDimension( "", Files[ "TentMap_rEDM.csv" ], None, "./", "",
"1 100", "201 500", 10, 1, 1,
"TentMap", "", False, False, 4 )
df = EDM.PredictInterval( "", Files[ "TentMap_rEDM.csv" ], None, "./", "",
"1 100", "201 500", 10, 2, 1,
"TentMap", "", False, False, 4 );
df = EDM.PredictNonlinear( "", Files[ "TentMapNoise_rEDM.csv" ], None,
"./", "", "1 100", "201 500", "", 2, 1, 0, 1,
"TentMap", "", False, False, 4 )
# Tent map simplex : specify multivariable columns embedded = True
df = EDM.Simplex( "", Files[ "block_3sp.csv" ], None, "./", "",
"1 99", "100 198", 3, 1, 0, 1, 0,
"x_t y_t z_t", "x_t", True, False, True, True )
# Tent map simplex : Embed column x_t to E=3, embedded = False
df = EDM.Simplex( "", Files[ "block_3sp.csv" ], None, "./", "",
"1 99", "100 195", 3, 1, 0, 1, 0,
"x_t", "x_t", False, False, True, True )
M = EDM.Multiview( "", Files[ "block_3sp.csv" ], None, "./", "",
"1 100", "101 198", 3, 1, 0, 1,
"x_t y_t z_t", "x_t", 0, 0, False, 4, True )
# S-map circle : specify multivariable columns embedded = True
S = EDM.SMap( "", Files[ "circle.csv" ], None, "./", "",
"1 100", "101 198", 2, 1, 0, 1, 4, 0,
"x y", "x", "", "", True, False, True, True )
df = EDM.CCM( "", Files[ "sardine_anchovy_sst.csv" ], None, "./", "",
3, 0, 0, 1, "anchovy", "np_sst",
"10 70 10", 100, True, False, 0, True, True )
def main():
'''EDM package examples.
df denotes a Pandas DataFrame.
S and M denote dictionaries of Pandas DataFrames'''
dataFiles = [ "TentMap_rEDM.csv",
"TentMapNoise_rEDM.csv",
"circle.csv",
"block_3sp.csv",
"sardine_anchovy_sst.csv" ]
# Create map of module dataFiles pathnames in Files
Files = {}
for file in dataFiles:
filename = "data/" + file
if pkg_resources.resource_exists( EDM.__name__, filename ):
Files[ file ] = \
pkg_resources.resource_filename( EDM.__name__, filename )
else :
raise Exception( "examples.py: Failed to find data file " + \
file + " in EDM package" )
# Note the path argument is empty "", file path is in Files{}
df = EDM.EmbedDimension( pathIn = "", dataFile = Files["TentMap_rEDM.csv"],
lib = "1 100", pred = "201 500", maxE = 10,
Tp = 1, tau = -1, exclusionRadius = 0,
columns = "TentMap", target = "TentMap",
validLib = [], numThreads = 4 )
df = EDM.PredictInterval( pathIn = "", dataFile = Files["TentMap_rEDM.csv"],
lib = "1 100", pred = "201 500", maxTp = 10,
E = 2, tau = -1, exclusionRadius = 0,
columns = "TentMap", target = "TentMap",
validLib = [], numThreads = 4 );
df = EDM.PredictNonlinear( pathIn = "",
dataFile = Files[ "TentMapNoise_rEDM.csv" ],
lib = "1 100", pred = "201 500", E = 2,
Tp = 1, knn = 0, tau = -1,
columns = "TentMap", target = "TentMap",
validLib = [], numThreads = 4 )
# Tent map simplex : specify multivariable columns embedded = True
S = EDM.Simplex( pathIn = "", dataFile = Files[ "block_3sp.csv" ],
lib = "1 99", pred = "100 198", E = 3, Tp = 1,
knn = 0, tau = -1, exclusionRadius = 0,
columns = "x_t y_t z_t", target = "x_t",
embedded = True, showPlot = True,
validLib = [], generateSteps = 0, parameterList = False )
# Tent map simplex : Embed column x_t to E=3, embedded = False
S = EDM.Simplex( pathIn = "", dataFile = Files[ "block_3sp.csv" ],
lib = "1 99", pred = "100 198", E = 3, Tp = 1,
knn = 0, tau = -1, exclusionRadius = 0,
columns = "x_t y_t z_t", target = "x_t",
embedded = False, showPlot = True,
validLib = [], generateSteps = 0, parameterList = False )
M = EDM.Multiview( pathIn = "", dataFile = Files[ "block_3sp.csv" ],
lib = "1 100", pred = "101 198",
D = 0, E = 3, Tp = 1, knn = 0, tau = -1,
multiview = 0, exclusionRadius = 0,
columns = "x_t y_t z_t", target = "x_t",
trainLib = True, excludeTarget = False,
numThreads = 4, showPlot = True )
# S-map circle : specify multivariable columns embedded = True
S = EDM.SMap( pathIn = "", dataFile = Files[ "circle.csv" ],
lib = "1 100", pred = "101 198",
E = 2, Tp = 1, knn = 0, tau = -1,
theta = 4, exclusionRadius = 0,
columns = "x y", target = "x",
solver = None, embedded = True,
validLib = [], generateSteps = 0, parameterList = False )
CM = EDM.CCM( pathIn = "", dataFile = Files[ "sardine_anchovy_sst.csv" ],
E = 3, Tp = 0, knn = 0, tau = -1, exclusionRadius = 0,
columns = "anchovy", target = "np_sst",
libSizes = "10 70 10", sample = 100, random = True,
replacement = False, seed = 0, includeData = False,
parameterList = False, verbose = False, showPlot = True )