import os
import numpy as np
import pyopencl
from psyrun import Param
from psyrun.backend import LoadBalancingBackend
from cue.trials import HebbRepetitionTrial
rng = np.random.RandomState(39)
n_trials = 25
seeds = range(n_trials)
pspace = Param(extension=['forward-assoc', 'direct-assoc']) * Param(
seed=seeds, trial=range(n_trials))
backend = LoadBalancingBackend
exclude_from_result = ['cl_context']
pool_size = 4
max_jobs = 1
def setup(proc_id):
context = pyopencl.create_some_context(answers=[0, proc_id])
return {'cl_context': context}
def execute(trial, **kwargs):
result = HebbRepetitionTrial().run(progress=False, **kwargs)
return result
import numpy as np
from psyrun import Param
from psyrun.backend import LoadBalancingBackend
from psyrun.scheduler import Slurm
from cue.trials import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(seed=seeds,
trial=range(n_trials),
min_evidence=0.03,
noise=0.009,
distractor_rate=0.4,
ordinal_prob=0.1)
exclude_from_result = ['cl_context']
min_items = 1
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
pool_size = 1
max_jobs = 100
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:10'
else:
from psyrun import Param
from cogsci17_decide.trial import DecisionTrial
pspace = (
(Param(network=['LCA'], scale=[1.], share_thresholding_intercepts=[False])
+ Param(network=['IA']) * Param(share_thresholding_intercepts=[False]) *
Param(scale=[1.])) * Param(baseline=[0.2, 0.6, 1.0]) *
Param(target_sep=[0.05, 0.1, 0.15, 0.2]) *
Param(noise=[0.0, 0.01, 0.02, 0.03, 0.04, 0.05]) * Param(seed=range(50)))
min_items = 20
max_jobs = None
def execute(**kwargs):
return DecisionTrial().run(d=20, **kwargs)
import numpy as np
from psyrun import Param
from psyrun.backend import LoadBalancingBackend
from psyrun.scheduler import Slurm
from cue.trials import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(seed=seeds,
trial=range(n_trials),
recall_duration=45.,
noise=0.015,
min_evidence=0.025,
ordinal_prob=0.1)
exclude_from_result = ['cl_context']
min_items = 1
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
pool_size = 1
max_jobs = 100
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:59'
else:
import platform
import numpy as np
from psyrun import Param
from psyrun.scheduler import Slurm
from cue.trials import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(seed=seeds,
trial=range(n_trials),
recall_duration=45.,
noise=0.015,
min_evidence=0.04,
extension='disable_stm_recall')
min_items = 1
pool_size = 1
max_jobs = 100
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:59'
else:
return '0-02:59'
from psyrun import Param
from psyrun import LoadBalancingBackend
from psyrun.scheduler import Slurm
from cue.trials import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(
seed=seeds,
trial=range(n_trials),
noise=0.015,
min_evidence=0.025,
ordinal_prob=1.)
exclude_from_result = ['cl_context']
min_items = 1
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
pool_size = 1
max_jobs = 100
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:10'
else:
return '0-02:59'
import platform
import numpy as np
from psyrun import Param
from psyrun.scheduler import Slurm, Sqsub
from cue.trials.default import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(seed=seeds,
trial=range(n_trials),
recall_duration=90.,
noise=0.015,
min_evidence=0.02)
min_items = 1
pool_size = 1
max_jobs = 100
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:59'
else:
return '0-06:00'
import numpy as np
from psyrun import Param
from psyrun.backend import LoadBalancingBackend
from psyrun.scheduler import Slurm
from cue.trials import CueTrial
rng = np.random.RandomState(846777)
n_trials = 100
seeds = range(100)
pspace = Param(seed=seeds,
trial=range(n_trials),
min_evidence=0.0325,
noise=0.015,
distractor_rate=0.3,
ordinal_prob=0.0)
exclude_from_result = ['cl_context']
min_items = 1
if platform.node().startswith('gra') or platform.node().startswith('cedar'):
pool_size = 1
max_jobs = 100
workdir = '/scratch/jgosmann/cue'
scheduler = Slurm(workdir)
def timelimit(name):
if 'split' in name or 'merge' in name:
return '0-00:10'
else:
import platform
import sys
import_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', 'sparat'))
if import_path not in sys.path:
sys.path.insert(0, import_path)
import numpy as np
from psyrun import Param
from psyrun.scheduler import Sqsub
from model.benchmark import ConnectionsRatModel
neurons_per_dimension = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
pspace = Param(neurons_per_dimension=neurons_per_dimension, seed=923)
min_items = 1
max_splits = 100
sharcnet_nodes = ['narwhal', 'bul', 'kraken', 'saw']
if any(platform.node().startswith(x) for x in sharcnet_nodes):
workdir = '/work/jgosmann/rat'
scheduler = Sqsub(workdir)
scheduler_args = {
'timelimit': '60m',
'memory': '6G',
}
def execute(**kwargs):
import numpy as np
import pyopencl
from psyrun import Param
from psyrun.backend import LoadBalancingBackend
from cue.trials import MixedSelTrial
rng = np.random.RandomState(9)
n_sequences = 6
seeds = [230] * n_sequences
pspace = Param(seed=seeds, sequence=range(n_sequences))
backend = LoadBalancingBackend
exclude_from_result = ['cl_context']
pool_size = 4
max_jobs = 1
def setup(proc_id):
context = pyopencl.create_some_context(answers=[1, proc_id])
return {'cl_context': context}
def execute(**kwargs):
result = MixedSelTrial().run(progress=False, **kwargs)
return result