SYMBOL INDEX (77 symbols across 15 files)

FILE: battery_storage/calc_stats.py
  function load_results (line 5) | def load_results(load_folders):
  function get_means_stds (line 36) | def get_means_stds(df):
  function calc_stats (line 41) | def calc_stats(load_folders, save_folder):

FILE: battery_storage/main.py
  function main (line 27) | def main():
  function init_params (line 91) | def init_params(param_set):
  function get_features_labels (line 122) | def get_features_labels(params):
  function get_train_test_split (line 187) | def get_train_test_split(params, train_frac):
  function get_loaders_tt (line 199) | def get_loaders_tt(arrays_dict, bsz):
  function get_loaders_tth (line 206) | def get_loaders_tth(arrays_dict, bsz):
  function get_train_hold_split (line 215) | def get_train_hold_split(tensors_dict, th_frac, save_folder):

FILE: battery_storage/model_classes.py
  class Net (line 17) | class Net(nn.Module):
    method __init__ (line 18) | def __init__(self, X, Y, hidden_layer_sizes, T):
    method forward (line 39) | def forward(self, x):
  class ScheduleBattery (line 42) | class ScheduleBattery(nn.Module):
    method __init__ (line 45) | def __init__(self, params):
    method forward (line 87) | def forward(self, log_prices):

FILE: battery_storage/nets.py
  function task_loss (line 16) | def task_loss(sched, Y_actual, params):
  function rmse_loss (line 29) | def rmse_loss(mu_pred, Y_actual):
  function run_rmse_net (line 33) | def run_rmse_net(model, loaders, params, tensors_task):
  function run_task_net (line 117) | def run_task_net(model, loader, params, args, tensors_task):
  function eval_for_loaders (line 192) | def eval_for_loaders(which, model, loaders, params, save_folder, loader_...
  function eval_net (line 230) | def eval_net(which, model, loaders, params, save_folder):

FILE: newsvendor/batch.py
  function get_vars (line 11) | def get_vars(batch_sz, X_test_t, Y_test_t):
  function get_vars_scalar_out (line 17) | def get_vars_scalar_out(batch_sz, X_test_t, Y_test_t):
  function get_cost_helper (line 24) | def get_cost_helper(batch_sz, epoch, model, X_test_t, Y_test_t,
  function get_cost (line 55) | def get_cost(batch_sz, epoch, model, X_test_t, Y_test_t, loss_fn):
  function get_cost_nll (line 59) | def get_cost_nll(batch_sz, epoch, model, X_test_t, Y_test_t, loss_fn):

FILE: newsvendor/main.py
  function main (line 24) | def main():
  function init_newsvendor_params (line 169) | def init_newsvendor_params():
  function init_theta_true (line 193) | def init_theta_true(params, is_linear=True, with_seed=False):
  function gen_data (line 210) | def gen_data(m, params, Theta_true_lin, Theta_true_sq, with_seed=False):
  function log_error_and_write (line 227) | def log_error_and_write(e, save_folder, m, run, model, results_file, new...

FILE: newsvendor/mle.py
  function linear_softmax_reg (line 12) | def linear_softmax_reg(X, Y, params):
  function newsvendor_opt (line 23) | def newsvendor_opt(params, py):
  function f_obj (line 35) | def f_obj(z, d, params):
  function newsvendor_eval (line 43) | def newsvendor_eval(X, Y, Theta_lin, Theta_sq, params):

FILE: newsvendor/mle_net.py
  class SolveNewsvendor (line 22) | class SolveNewsvendor(nn.Module):
    method __init__ (line 24) | def __init__(self, params, eps=1e-2):
    method forward (line 48) | def forward(self, y):
  function get_model (line 69) | def get_model(X_train, Y_train, X_test, Y_test, params, is_nonlinear):
  function run_mle_net (line 89) | def run_mle_net(X, Y, X_test, Y_test, params, is_nonlinear=False):
  function batch_train (line 198) | def batch_train(batch_sz, epoch, X_train_t, Y_train_t, Y_train_int_t,

FILE: newsvendor/plot.py
  function plot_results (line 19) | def plot_results(save_folder, true_model):

FILE: newsvendor/policy_net.py
  function run_policy_net (line 17) | def run_policy_net(X_train, Y_train, X_test, Y_test, params, is_nonlinea...
  function batch_train (line 67) | def batch_train(batch_sz, epoch, X_train_t, Y_train_t, model, opt, cost_...

FILE: newsvendor/task_net.py
  class SolveNewsvendor (line 22) | class SolveNewsvendor(nn.Module):
    method __init__ (line 24) | def __init__(self, params, eps=1e-2):
    method forward (line 48) | def forward(self, y):
  function run_task_net (line 68) | def run_task_net(X, Y, X_test, Y_test, params, is_nonlinear=False):
  function batch_train (line 171) | def batch_train(batch_sz, epoch, X_train_t, Y_train_t, Y_train_int_t,

FILE: power_sched/main.py
  function main (line 27) | def main():
  function load_data_with_features (line 111) | def load_data_with_features(filename):

FILE: power_sched/model_classes.py
  class Net (line 17) | class Net(nn.Module):
    method __init__ (line 18) | def __init__(self, X, Y, hidden_layer_sizes):
    method forward (line 40) | def forward(self, x):
    method set_sig (line 44) | def set_sig(self, X, Y):
  function GLinearApprox (line 50) | def GLinearApprox(gamma_under, gamma_over):
  function GQuadraticApprox (line 77) | def GQuadraticApprox(gamma_under, gamma_over):
  class SolveSchedulingQP (line 106) | class SolveSchedulingQP(nn.Module):
    method __init__ (line 108) | def __init__(self, params):
    method forward (line 119) | def forward(self, z0, mu, dg, d2g):
  class SolveScheduling (line 132) | class SolveScheduling(nn.Module):
    method __init__ (line 135) | def __init__(self, params):
    method forward (line 148) | def forward(self, mu, sig):

FILE: power_sched/nets.py
  function task_loss (line 16) | def task_loss(Y_sched, Y_actual, params):
  function task_loss_no_mean (line 21) | def task_loss_no_mean(Y_sched, Y_actual, params):
  function rmse_loss (line 26) | def rmse_loss(mu_pred, Y_actual):
  function rmse_loss_weighted (line 29) | def rmse_loss_weighted(mu_pred, Y_actual, weights):
  function run_rmse_net (line 33) | def run_rmse_net(model, variables, X_train, Y_train):
  function run_weighted_rmse_net (line 56) | def run_weighted_rmse_net(X_train, Y_train, X_test, Y_test, params):
  function run_weighted_rmse_net_helper (line 63) | def run_weighted_rmse_net_helper(X_train, Y_train, X_test, Y_test, param...
  function batch_train_weightrmse (line 91) | def batch_train_weightrmse(batch_sz, epoch, X_train_t, Y_train_t, model,...
  function run_task_net (line 122) | def run_task_net(model, variables, params, X_train, Y_train, args):
  function eval_net (line 179) | def eval_net(which, model, variables, params, save_folder):

FILE: power_sched/plot.py
  function load_results (line 16) | def load_results(load_folders):
  function get_means_stds (line 43) | def get_means_stds(df):
  function plot_results (line 46) | def plot_results(load_folders, save_folder):