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UNIQORN -- The Universal Neural-network Interface for Quantum Observable Readout from N-body wavefunctions
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| | Error_from_formula.NSPS = np.arange(10, 101, 10, dtype=int) |
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| list | Error_from_formula.scores = [] |
| |
| | Error_from_formula.Learn |
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| | Error_from_formula.momLabels |
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| | Error_from_formula.momData |
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| | Error_from_formula.NDatasets |
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| | Error_from_formula.DataString = SpaceString(inp.momData) |
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| | Error_from_formula.LabelString = SpaceString(inp.momLabels) |
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| string | Error_from_formula.fname = 'Errors_'+inp.Learn+'_in_'+LabelString+'_from_SSS_in_'+DataString+'_from_formula_'+str(inp.NDatasets)+'_datasets' |
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| | Error_from_formula.NShotsPerSample |
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| | Error_from_formula.NShots |
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| | Error_from_formula.training_generator |
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| | Error_from_formula.validation_generator |
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| | Error_from_formula.X_train |
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| | Error_from_formula.y_train |
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| | Error_from_formula.X_val |
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| | Error_from_formula.y_val = np.array(y_val) |
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| | Error_from_formula.x_data = np.reshape(np.concatenate((np.array(X_train),np.array(X_val))),(inp.NDatasets,256,i)) |
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| | Error_from_formula.y_data = np.reshape(np.concatenate((np.array(y_train),np.array(y_val))),(inp.NDatasets,256*256)) |
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| list | Error_from_formula.thiserr = [] |
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| list | Error_from_formula.rho2 = [] |
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| float | Error_from_formula.local_err = y_data[j,:]/83.0-np.reshape(rho2,inp.Npoints*inp.Npoints) |
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| list | Error_from_formula.rho = [] |
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| list | Error_from_formula.legend = [] |
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| | Error_from_formula.fig |
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| | Error_from_formula.ax |
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| | Error_from_formula.yerr |
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| | Error_from_formula.fmt |
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| | Error_from_formula.ecolor |
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| | Error_from_formula.loc |
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| | Error_from_formula.xlabel |
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| | Error_from_formula.ylabel |
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def ApplyInterpolation(y_data, y_data_interpolation)
Definition: ModelTrainingAndValidation.py:70
def data_refiner(x_data, y_data)
DATA EXTRACTION #########.
Definition: DataPreprocessing.py:82
def data_ID_loader(**kwargs)
IMPORT DATASET AND LABELS.
Definition: DataLoading.py:34
def main(x_data, y_data)
Definition: DataPreprocessing.py:21
list_IDs
Definition: DataGenerator.py:109
def grid_display(list_of_images, list_of_titles=[], no_of_columns=2, figsize=(10, 10))
Definition: Visualization.py:572
def circular_var(phases)
Definition: rel_phase_package.py:126
def folder_creation()
Definition: Visualization.py:677
def SaveHistoryToFile(HISTORY, keys, HistoryFileName)
Definition: Visualization.py:536
def manipulate_super_regr(x_data, y_data, NDatasetsNew)
DATA MANIPULATION FOR SUPERVISED REGRESSION #########.
Definition: DataPreprocessing.py:180
def ModelEvaluation(*args)
Definition: ModelTrainingAndValidation.py:415
def get_configspace()
Definition: HyperParameterOpt_Worker.py:98
def plot_predictions(y_test, test_predictions, NViz, ss_label, space_label)
PLOT OF THE PREDICTIONS FOR SUPERVISED REGRESSION FROM SINGLE SHOTS ###.
Definition: Visualization.py:198
def SpaceString(selector)
Definition: functions.py:13
int
Definition: HyperParameterOpt.py:22
def ApplyInterpolation(y_data, y_data_interpolation)
Definition: test_interpolate_spline.py:10
def LogCosh_plus_LogMSE_plus_MAE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:170
def LogCosh_plus_LogMSE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:158
def main(*args, **kwargs)
Definition: ModelTrainingAndValidation.py:177
def initial_fit(xdata, ydata, freq_min, bound, plot=1)
Definition: rel_phase_package.py:24
def scatterplot(phases, contrasts)
Definition: rel_phase_package.py:102
def randomize(a, b)
Definition: functions.py:85
def extract_info(filename, freq_min)
Definition: rel_phase_package.py:168
def replaceAll(file, searchExp, replaceExp)
Definition: functions.py:67
def normalizer(array)
Definition: functions.py:93
type
Definition: HyperParameterOpt.py:20
shuffle
Definition: DataGenerator.py:112
def gauss(x, amp, wid)
Definition: rel_phase_package.py:9
def Rho2FromSS(SingleShots, NShots, Npoints)
Definition: functions.py:43
def Pmodel(x, amp, wid, con, freq, phase)
Definition: rel_phase_package.py:13
def RhoFromSS(SingleShots, NShots, Npoints)
Definition: functions.py:57
def DataThresholdSelection(IDs_tot, Low, High, NDatasets)
Definition: DataLoading.py:406
indexes
Definition: DataGenerator.py:140
def shape(tensor)
Definition: ModelTrainingAndValidation.py:53
def plot_history(history, ss_label, space_label)
Definition: Visualization.py:73
def __data_generation(self, list_IDs_temp, indexes)
Definition: DataGenerator.py:145
def G2FromSS(SingleShots, NShots, Npoints)
Definition: functions.py:23
def assign_variable_slice(slices, values, var)
Definition: test_interpolate_spline.py:41
def result_averaging(y_test, test_predictions)
Definition: Visualization.py:597
def LogCosh_plus_MAE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:152
def manipulate_super_class()
DATA MANIPULATION FOR SUPERVISED CLASSIFICATION #########.
Definition: DataPreprocessing.py:309
def circular_mean(phases)
Definition: rel_phase_package.py:135
def manipulate_unsuper_class()
DATA MANIPULATION FOR UNSUPERVISED CLASSIFICATION #######.
Definition: DataPreprocessing.py:335
def make_variable(tuple, initializer)
Definition: ModelTrainingAndValidation.py:49
def __init__(self, list_IDs, shuffle=True, batch_size=5)
Definition: DataGenerator.py:105
def MSE_plus_MAE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:142
def clear()
Definition: Models.py:30
def runtime_checks()
Definition: Runtime_check.py:7
def archive(Model_Name)
NEURAL NETWORK STRUCTURES IN DATABASE.
Definition: Models.py:282
def extractor(WhichData, DataFileName, K)
Definition: DataLoading.py:267
def default()
DEFAULT NEURAL NETWORK STRUCTURES.
Definition: Models.py:38
y_val
Definition: HyperParameterOpt_Worker.py:28
batch_size
Definition: DataGenerator.py:108
def __getitem__(self, index)
Definition: DataGenerator.py:121
def realtime_learning_curves(runs)
Definition: PlotInteractiveHyperParameterOpt.py:26
def InitializeDataGenerators()
DATA GENERATOR INITIALIZATION #########.
Definition: DataGenerator.py:22
def make_variable(tuple, initializer)
Definition: test_interpolate_spline.py:36
def plot_phase_histogram(phases, n_bins=15, hist_limits_x=None, hist_limits_y=None)
Definition: rel_phase_package.py:293
def RunThis(command)
Definition: functions.py:74
def label_ID_loader(DataNameComplete, **kwargs)
Definition: DataLoading.py:175
Definition: HyperParameterOpt_Worker.py:18
def Interpolation_MSE(y_true, y_pred)
Custom Loss function evaluating the error of an interpolation.
Definition: ModelTrainingAndValidation.py:96
str
Definition: HyperParameterOpt.py:24
float
Definition: HyperParameterOpt.py:20
def __init__(self, *args, sleep_interval=0, **kwargs)
Definition: HyperParameterOpt_Worker.py:20
def manipulate_unsuper_regr()
DATA MANIPULATION FOR UNSUPERVISED REGRESSION #########.
Definition: DataPreprocessing.py:322
Definition: DataGenerator.py:102
def histogrammer(data, LowerBound=-np.pi, UpperBound=np.pi, Bins=100)
Definition: functions.py:105
def custom(**kwargs)
CUSTOM NEURAL NETWORK STRUCTURES.
Definition: Models.py:119
def AddKeysToPlot(PLOT, HISTORY, keys, pattern, legend)
Definition: Visualization.py:559
def main(y_test, history, test_predictions, NViz)
Definition: Visualization.py:37
def on_epoch_end(self)
Definition: DataGenerator.py:138
sleep_interval
Definition: HyperParameterOpt_Worker.py:44
def fit_plot(mean, var)
Definition: rel_phase_package.py:141
def LogCosh_plus_MSE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:164
def compute(self, config, budget, **kwargs)
Definition: HyperParameterOpt_Worker.py:46
def __len__(self)
Definition: DataGenerator.py:116
def MSE_plus_LogMSE(y_true, y_pred)
Definition: ModelTrainingAndValidation.py:147
def get_labels()
Definition: Visualization.py:628
def plot_interpol(xesnew, ynew)
Definition: ModelTrainingAndValidation.py:58
1.8.17