from generation import transform
from Experiment import Experiment
from os import path
from tools import sec_to_time, gen_datestr
from time import time
from tools import check_folder
# Record start time for this experiment
start_time = time()
start_date = gen_datestr()
print(f"---- Started experiment : {start_date}")
# %%
# Determine name for save folder
report_folder = '/home/ronimber/PycharmProjects/temporal-coding/results/experiments/30_neurons/50_hz'
# Make sure the folder exists, create with all subfolders if it does not
check_folder(report_folder)
# Name of files from current condition
condition_name = 'interval=(1, 3)_relprob=0.25_relduration=6_'
# Main shared parameters
number_of_repetitions = 30
set_size = 200
fraction_training = 0.5
stimulus_duration = 500
# Machine learning model parameters
model_params = dict(
tau=2, # Voltage time decay constant
threshold=
0.025 # Threshold for firing, firing will result in a "1" classification
)
main_conditions) # Creating list with dictionary for each condition
for condition in conditions_list:
condition_params = shared_conditions.copy(
) # Grabbing the shared conditios
condition_params.update(condition) # Adding current conditions
# Setting the threshold and learning rate
condition_params['threshold'] = number_of_neuron_params[
condition['number_of_neurons']]['threshold']
condition_params['learning_rate'] = number_of_neuron_params[
condition['number_of_neurons']]['learning_rate']
# Determine location and name of generated template file
condition_report_folder = os.path.join(
save_folder, f"{condition['number_of_neurons']}_neurons",
f"{condition['frequency']}_hz")
check_folder(condition_report_folder)
condition_name = f"interval={condition_params['interval']}_relprob={condition_params['release_probability']}_relduration={condition_params['release_duration']}_"
# Adding to params dictionary
condition_params['report_folder'] = condition_report_folder
condition_params['condition_name'] = condition_name
target_file = os.path.join(condition_report_folder,
f'{condition_name}experiment_template.py')
# Creating template text
target_contents = template.substitute(condition_params)
# Saving template
with open(target_file, 'w') as file:
file.write(target_contents)
# target_contents = template.substitute(substitutions)
print(f"---- Started experiment : {start_date}")
# %% Specific parameters for parameter selection, easier to control from here
threshold = 250e-3
learning_rate = 5e-5
# %% Parameter specification
# Set location of root report (results) folder
# report_folder = '/home/ron/OneDrive/Documents/Masters/Parnas/temporal-coding/Results/' # For laptop
report_folder = '/home/ronimber/PycharmProjects/temporal-coding/Results/' # For Google-Compute-Engine
# Optional: Set ordered sub-folders according to condition keywords
condition_folders = ('parameter_selection', '30_neurons', '15_hz',
'interval_(1-3)') # If empty, saves at report folder
# Determine name for save folder
save_folder = path.join(report_folder, *condition_folders)
# Make sure the folder exists, create with all subfolders if it does not
check_folder(save_folder)
# Optional: Set custom name for the experiment, useful when running several experiments with some shared conditions,
# All experiment related files will have this name prepended to them with an underscore
experiment_name = f'thresh={threshold}_lrnRate={learning_rate}' # This can be left empty to save without experiment name
# Base experiment parameters
number_of_repetitions = 10 # Number of times to repeat the experiment with the exact same conditions
set_size = 200 # The size of the set(s) to generate for this experiment
fraction_training = 0.5 # Fraction of samples to be used in training, the rest go to testing
stimulus_duration = 500 # Maximal duration of the stimulus
# Machine learning model parameters
model_params = dict(
tau=2, # Voltage time decay constant
threshold=
the spikes in the original stimulus are allowed to shift, for
this purpose a symmetric interval shift is used, allowing
each spike to move forward or backward in time within a specified interval.
'''
# %%
from generation import make_stimulus
from generation.transform import symmetric_interval_shift
from tools import calc_stimuli_distance, check_folder, time_tools
import numpy as np
import os
import datetime
# %%
# Set up file saving
target_folder = '/mnt/disks/data' # Set up where the sets will be saved
check_folder(target_folder)
# Stimuli generation parameters
frequencies = [15, 50, 100]
number_of_neurons = [30, 150, 500]
duration = 500 # Units: ms
# Pair generation parameters
number_of_pairs = 100 # Number of stimulus pairs for each interval condition
# Intervals to use for temporal shifting
temporal_shift_intervals = [[3, 5], [3, 7],
[3, 10], [3, 15]] # Units: Seconds
# %%
current_date = time_tools(with_time=False)
for num_neur in number_of_neurons:
