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diff --git a/.gitignore b/.gitignore
index 39be17c..9c9788c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,11 @@
/jax_test
/dataset
/.vscode
/ecg_syn/*.dat
/ecg_syn/*.opt
/dataset
-/src/__pycache__/*
\ No newline at end of file
+/src/__pycache__/*
+best*
+worst*
+loss:*
+/img_res/
\ No newline at end of file
diff --git a/README.MD b/README.MD
index c72e840..a346a8e 100644
--- a/README.MD
+++ b/README.MD
@@ -1,3 +1,6 @@
-1: The main processing pipeline entrypoint, contained in src, is "gaussian_collapsing.py".
-2: Setting DEV_MODE to True allow to define parameters and display results, turning it to False will start the processing pipeline training
-3: The parameters for the processing pipeline are contained in the defined constants at the top of "gaussian_collapsing.py"
\ No newline at end of file
+# gaussian_collapsing.py\
+1: The main processing pipeline entrypoint, contained in src, is "gaussian_collapsing.py".\
+2: Setting DEV_MODE to True allow to define parameters and display results, turning it to False will start the processing pipeline training\
+3: The parameters for the processing pipeline are contained in the defined constants at the top of "gaussian_collapsing.py"\
+# gaussian_collapsing_no_batch.py\
+Same as previously, the settings for the process are contained in the defined constant at the top of the script
\ No newline at end of file
diff --git a/src/best_loss:0.1196836456656456_sigma:0.02306805195280425_time:17223135.09.svg b/src/best_loss:0.1196836456656456_sigma:0.02306805195280425_time:17223135.09.svg
deleted file mode 100644
index 7bf8986..0000000
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diff --git a/src/gaussain_collpsing.py b/src/gaussain_collpsing.py
index 2cc7c8b..d52f2fd 100644
--- a/src/gaussain_collpsing.py
+++ b/src/gaussain_collpsing.py
@@ -1,252 +1,263 @@
from functools import partial
import os
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from jax import grad, jit, pmap, value_and_grad, vmap
from jax.scipy.special import logsumexp
from torch.utils.data import DataLoader
import optax
import dataset_generator as dataLoader
import processing as proc
-FREQ = 20
-NUM_SAMPLES = 5_000
-BATCH_SIZE = 1_000
+FREQ = 1000
+NUM_SAMPLES = 5_00
+BATCH_SIZE = 1_00
USE_ENERGY_MAX_FREQ_EVAL = True
STOPPING_PERC = 1000
INTERPOLATOR = 'sinc'
SIGNAL_TYPE = 'create' #'create': create a new emyulated ECG signal, 'load': load previous ECG signal, 'random': random sinusoidal mix signal
EPOCHS = 6
SIGMA_EPOCH = 6
-SIGMA_INIT_DIVISOR = 15
+SIGMA_INIT_DIVISOR = 2
SIGMA_EPOCH_DIVISOR = 1.7
INIT_FREQ_DIVISOR = 3
NUM_LVLS = 4
-LR = 0.01
+LR = 0.1
-DEV_MODE = True
+DEV_MODE = False
+imag_res_folder = "../img_res"
def init_pipeline(gaussian_numbers,range_min,range_max,freq):
mu_s = jnp.linspace(range_min+(range_max-range_min)/16,range_max-(range_max-range_min)/16,gaussian_numbers)
params = {'mus':mu_s,'freq':freq}
return params
def proc_pipeline(params,x,sigma,static_params):
signal = x
signal = proc.mix_gaussian_lvl_crossing(signal,params['mus'],sigma)
signal = proc.normalize(signal)
signal = proc.sinc_interpolation_freq_parametrized(signal,params['freq'],FREQ,static_params['time_base'])
signal = proc.normalize(signal)
return signal
batched_proc_pipeline = pmap(proc_pipeline,in_axes=(None,0,None,None),static_broadcasted_argnums=[3])
batched_RMSE = pmap(proc.RMSE)
def batched_loss_fn(params, data, nyquist_sampled_data, sigma, static_params):
proc_results = batched_proc_pipeline(params, data,sigma, static_params)
return jnp.mean(batched_RMSE(proc_results,nyquist_sampled_data))
def compute_loss(dataset,params,sigma,static_params):
loss = 0
i = 0
for (batch,objective_batch) in dataset:
i+=1
loss += batched_loss_fn(params, batch, objective_batch,sigma, static_params)
return loss/i
def train(train_dataset,params,sigma,static_params, lr=LR):
print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
optimizer = optax.adam(lr)
opt_state = optimizer.init(params)
for e in range(EPOCHS):
print(f"Epoch {e+1}")
for (train_batch,objective_batch) in train_dataset:
grads = grad(batched_loss_fn)(params, train_batch, objective_batch, sigma, static_params)
updates, opt_state = optimizer.update(grads, opt_state)
params = optax.apply_updates(params, updates)
print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
return params #Final loss
def compute_loss_each_samples(dataset,params,sigma,static_params): #NO
loss = []
for (batch,objective_batch) in dataset:
proc_results = batched_proc_pipeline(params, batch,sigma, static_params)
loss.extend(batched_RMSE(proc_results, objective_batch))
return loss
def compute_transform_each_samples(train_dataset,params,sigma,static_params, lr=LR):
proc_results = []
for (train_batch,_) in train_dataset:
proc_results.extend(batched_proc_pipeline(params, train_batch,sigma, static_params))
return proc_results #Final loss
def custom_collate_fn(batch):
transposed_data = list(zip(*batch))
data = np.array(transposed_data[0])
obj = np.array(transposed_data[1])
return data, obj
class hashabledict(dict):
def __hash__(self):
return hash(tuple(sorted(self.items())))
class hashable_np_array(np.ndarray):
def __hash__(self):
return int(self.mean()*1_000_000_000)
def main():
if DEV_MODE:
freq_desired = int(input("What base frequency do you need your data: "))
mus_num = int(input("How many levels: "))
sigma_div = int(input("Sigma fraction of the signal range (1/X): "))
nyq_freq_div = int(input("Sync freq in fraction of nyq. freq: "))
data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=1,freq=freq_desired)
print("Computing Nyquist frequency...")
nyq_freq = proc.get_nyquist_freq_dataset(data,freq_desired,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/freq_desired,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/freq_desired,1/freq_desired)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective")
dataset_nyq = proc.interpolate_dataset(data, freq_desired, nyq_freq)
print("Initializing pipelines Parameters")
sigma = float((jnp.max(data)-jnp.min(data))/sigma_div)
params = init_pipeline(mus_num,np.min(data),np.max(data),nyq_freq/nyq_freq_div)
print(f"Parameters: {params}\nSigma: {sigma}")
print("Computing mixed gaussian representation")
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[0],params['mus'],sigma)
print("Normalizing")
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
print("Computing sync filter")
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],freq_desired,t_base_orig)
print("Normalizing")
resampled_gaussians = proc.normalize(resampled_gaussians)
print("Plotting")
plt.figure()
plt.plot(t_base_orig, data[0])
plt.plot(t_base_nyq,dataset_nyq[0],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
plt.show()
return
+
+ #Setting env:
+ if not os.path.isdir(imag_res_folder):
+ os.mkdir(imag_res_folder)
+ t_stamp = str(os.times().elapsed)
+ res_folder_this_run = os.path.join(imag_res_folder,t_stamp)
+ os.mkdir(res_folder_this_run)
+ os.environ["XLA_FLAGS"] = f'--xla_force_host_platform_device_count={BATCH_SIZE}'
+
#load/generate data
print("Loading data")
- os.environ["XLA_FLAGS"] = f'--xla_force_host_platform_device_count={BATCH_SIZE}'
data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=NUM_SAMPLES,freq=FREQ)
if data is None:
print("Failed loading data")
return
data = proc.normalize_dataset(data)
print("\n-------------------\n")
print(f"Loaded Data:shape {data.shape}")
#reference
print("Computing maximum Nyquist frequency for the Dataset...")
nyq_freq = proc.get_nyquist_freq_dataset(data,FREQ,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/FREQ,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/FREQ,1/FREQ)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective dataset")
dataset_nyq = proc.interpolate_dataset(data, FREQ, nyq_freq)
train_dataset = [[d,o] for d,o in zip(data,dataset_nyq)]
static_params = hashabledict({'freq' : None,'time_base' : None})
static_params['freq'] = nyq_freq
static_params['time_base'] = t_base_nyq.view(hashable_np_array)
#static_params = t_base_nyq.view(hashable_np_array)
#INIT
print("Initializing pipelines Parameters")
sigma = float((jnp.max(data)-jnp.min(data))/SIGMA_INIT_DIVISOR)
params = init_pipeline(NUM_LVLS,np.min(data),np.max(data),nyq_freq/INIT_FREQ_DIVISOR)
train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, collate_fn=custom_collate_fn, drop_last=False)
print(f"Parameters: {params}")
loss_sigmas = []
for i in range(SIGMA_EPOCH):
print(f"SIGMA EPOCH: {i+1}/{SIGMA_EPOCH}, sigma: {sigma}")
params = train(train_loader,params,sigma,static_params)
loss = compute_loss(train_loader,params,sigma,static_params)
loss_sigmas.append(loss)
losses = compute_loss_each_samples(train_loader,params,sigma,static_params)
best_beat = np.argmin(losses)
worst_beat = np.argmax(losses)
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[best_beat],params['mus'],sigma)
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
resampled_gaussians = proc.normalize(resampled_gaussians)
plt.figure()
plt.plot(t_base_orig, data[best_beat])
plt.plot(t_base_nyq,dataset_nyq[best_beat],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./best_loss:{np.min(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
+ plt.savefig(f'{res_folder_this_run}/sigma:{sigma}_best_loss:{np.min(losses)}.svg')
plt.close()
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[worst_beat],params['mus'],sigma)
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
resampled_gaussians = proc.normalize(resampled_gaussians)
plt.figure()
plt.plot(t_base_orig, data[worst_beat])
plt.plot(t_base_nyq,dataset_nyq[worst_beat],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./worst_loss:{np.max(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
+ plt.savefig(f'{res_folder_this_run}/sigma:{sigma}_worst_loss:{np.max(losses)}.svg')
plt.close()
sigma /= SIGMA_EPOCH_DIVISOR
print(f"END OF SIGMA EPOCH {i+1}, LOSS = {loss}")
print(params)
+ plt.figure()
plt.plot(loss_sigmas)
- plt.show()
+ plt.savefig(f'{res_folder_this_run}/lossVSepoch.svg')
+ plt.close()
pt = 3
#create gaussain level crossing:
mu_s = params['mus']
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[pt],mu_s,sigma)
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
resampled_gaussians = proc.normalize(resampled_gaussians)
#Testing grounds
plt.plot(t_base_orig, data[pt])
plt.plot(t_base_nyq,dataset_nyq[pt],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.show()
if __name__ == "__main__":
#test_signal()
main()
diff --git a/src/gaussain_collpsing.py b/src/gaussain_collpsing_no_batch.py
similarity index 63%
copy from src/gaussain_collpsing.py
copy to src/gaussain_collpsing_no_batch.py
index 2cc7c8b..78a864e 100644
--- a/src/gaussain_collpsing.py
+++ b/src/gaussain_collpsing_no_batch.py
@@ -1,252 +1,210 @@
from functools import partial
import os
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from jax import grad, jit, pmap, value_and_grad, vmap
from jax.scipy.special import logsumexp
from torch.utils.data import DataLoader
import optax
import dataset_generator as dataLoader
import processing as proc
-FREQ = 20
-NUM_SAMPLES = 5_000
-BATCH_SIZE = 1_000
+FREQ = 1000
+#NUM_SAMPLES = 5_00
+#BATCH_SIZE = 1_00
USE_ENERGY_MAX_FREQ_EVAL = True
STOPPING_PERC = 1000
INTERPOLATOR = 'sinc'
SIGNAL_TYPE = 'create' #'create': create a new emyulated ECG signal, 'load': load previous ECG signal, 'random': random sinusoidal mix signal
-EPOCHS = 6
+EPOCHS = 200
SIGMA_EPOCH = 6
-SIGMA_INIT_DIVISOR = 15
+SIGMA_INIT_DIVISOR = 2
SIGMA_EPOCH_DIVISOR = 1.7
INIT_FREQ_DIVISOR = 3
-NUM_LVLS = 4
-LR = 0.01
+NUM_LVLS = 8
+LR = 0.05
-DEV_MODE = True
+DEV_MODE = False
+imag_res_folder = "../img_res"
def init_pipeline(gaussian_numbers,range_min,range_max,freq):
mu_s = jnp.linspace(range_min+(range_max-range_min)/16,range_max-(range_max-range_min)/16,gaussian_numbers)
params = {'mus':mu_s,'freq':freq}
return params
def proc_pipeline(params,x,sigma,static_params):
signal = x
signal = proc.mix_gaussian_lvl_crossing(signal,params['mus'],sigma)
signal = proc.normalize(signal)
signal = proc.sinc_interpolation_freq_parametrized(signal,params['freq'],FREQ,static_params['time_base'])
signal = proc.normalize(signal)
return signal
-batched_proc_pipeline = pmap(proc_pipeline,in_axes=(None,0,None,None),static_broadcasted_argnums=[3])
-batched_RMSE = pmap(proc.RMSE)
+@partial(jax.jit, static_argnums=(4,))
+def loss_one_sample(params,x,obj,sigma,static_params):
+ proc_res = proc_pipeline(params,x,sigma,static_params)
+ return proc.RMSE(proc_res,obj)
-def batched_loss_fn(params, data, nyquist_sampled_data, sigma, static_params):
- proc_results = batched_proc_pipeline(params, data,sigma, static_params)
- return jnp.mean(batched_RMSE(proc_results,nyquist_sampled_data))
-
-def compute_loss(dataset,params,sigma,static_params):
- loss = 0
- i = 0
- for (batch,objective_batch) in dataset:
- i+=1
- loss += batched_loss_fn(params, batch, objective_batch,sigma, static_params)
- return loss/i
-
-def train(train_dataset,params,sigma,static_params, lr=LR):
- print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
+def train(sample,objective,params,sigma,static_params, lr=LR):
+ print(f"Loss: {loss_one_sample(params,sample,objective,sigma,static_params)}")
optimizer = optax.adam(lr)
opt_state = optimizer.init(params)
for e in range(EPOCHS):
print(f"Epoch {e+1}")
- for (train_batch,objective_batch) in train_dataset:
- grads = grad(batched_loss_fn)(params, train_batch, objective_batch, sigma, static_params)
- updates, opt_state = optimizer.update(grads, opt_state)
- params = optax.apply_updates(params, updates)
- print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
+ grads = grad(loss_one_sample)(params,sample,objective,sigma,static_params)
+ updates, opt_state = optimizer.update(grads, opt_state)
+ params = optax.apply_updates(params, updates)
+ print(f"Loss: {loss_one_sample(params,sample,objective,sigma,static_params)}")
return params #Final loss
-def compute_loss_each_samples(dataset,params,sigma,static_params): #NO
- loss = []
- for (batch,objective_batch) in dataset:
- proc_results = batched_proc_pipeline(params, batch,sigma, static_params)
- loss.extend(batched_RMSE(proc_results, objective_batch))
- return loss
-
-def compute_transform_each_samples(train_dataset,params,sigma,static_params, lr=LR):
- proc_results = []
- for (train_batch,_) in train_dataset:
- proc_results.extend(batched_proc_pipeline(params, train_batch,sigma, static_params))
- return proc_results #Final loss
-
-
-
-def custom_collate_fn(batch):
- transposed_data = list(zip(*batch))
- data = np.array(transposed_data[0])
- obj = np.array(transposed_data[1])
- return data, obj
class hashabledict(dict):
def __hash__(self):
return hash(tuple(sorted(self.items())))
class hashable_np_array(np.ndarray):
def __hash__(self):
return int(self.mean()*1_000_000_000)
def main():
if DEV_MODE:
freq_desired = int(input("What base frequency do you need your data: "))
mus_num = int(input("How many levels: "))
sigma_div = int(input("Sigma fraction of the signal range (1/X): "))
nyq_freq_div = int(input("Sync freq in fraction of nyq. freq: "))
data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=1,freq=freq_desired)
print("Computing Nyquist frequency...")
nyq_freq = proc.get_nyquist_freq_dataset(data,freq_desired,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/freq_desired,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/freq_desired,1/freq_desired)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective")
dataset_nyq = proc.interpolate_dataset(data, freq_desired, nyq_freq)
print("Initializing pipelines Parameters")
sigma = float((jnp.max(data)-jnp.min(data))/sigma_div)
params = init_pipeline(mus_num,np.min(data),np.max(data),nyq_freq/nyq_freq_div)
print(f"Parameters: {params}\nSigma: {sigma}")
print("Computing mixed gaussian representation")
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[0],params['mus'],sigma)
print("Normalizing")
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
print("Computing sync filter")
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],freq_desired,t_base_orig)
print("Normalizing")
resampled_gaussians = proc.normalize(resampled_gaussians)
print("Plotting")
plt.figure()
plt.plot(t_base_orig, data[0])
plt.plot(t_base_nyq,dataset_nyq[0],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
plt.show()
return
+
+ #Setting env:
+ if not os.path.isdir(imag_res_folder):
+ os.mkdir(imag_res_folder)
+ t_stamp = str(os.times().elapsed)
+ res_folder_this_run = os.path.join(imag_res_folder,t_stamp)
+ os.mkdir(res_folder_this_run)
+
#load/generate data
print("Loading data")
- os.environ["XLA_FLAGS"] = f'--xla_force_host_platform_device_count={BATCH_SIZE}'
- data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=NUM_SAMPLES,freq=FREQ)
+ data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=1,freq=FREQ)
if data is None:
print("Failed loading data")
return
data = proc.normalize_dataset(data)
print("\n-------------------\n")
print(f"Loaded Data:shape {data.shape}")
#reference
print("Computing maximum Nyquist frequency for the Dataset...")
nyq_freq = proc.get_nyquist_freq_dataset(data,FREQ,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/FREQ,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/FREQ,1/FREQ)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective dataset")
dataset_nyq = proc.interpolate_dataset(data, FREQ, nyq_freq)
train_dataset = [[d,o] for d,o in zip(data,dataset_nyq)]
static_params = hashabledict({'freq' : None,'time_base' : None})
static_params['freq'] = nyq_freq
static_params['time_base'] = t_base_nyq.view(hashable_np_array)
#static_params = t_base_nyq.view(hashable_np_array)
#INIT
print("Initializing pipelines Parameters")
sigma = float((jnp.max(data)-jnp.min(data))/SIGMA_INIT_DIVISOR)
params = init_pipeline(NUM_LVLS,np.min(data),np.max(data),nyq_freq/INIT_FREQ_DIVISOR)
- train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, collate_fn=custom_collate_fn, drop_last=False)
print(f"Parameters: {params}")
loss_sigmas = []
for i in range(SIGMA_EPOCH):
print(f"SIGMA EPOCH: {i+1}/{SIGMA_EPOCH}, sigma: {sigma}")
-
- params = train(train_loader,params,sigma,static_params)
- loss = compute_loss(train_loader,params,sigma,static_params)
+ params = train(train_dataset[0][0],train_dataset[0][1],params,sigma,static_params)
+ loss = loss_one_sample(params,train_dataset[0][0],train_dataset[0][1],sigma,static_params)
loss_sigmas.append(loss)
-
- losses = compute_loss_each_samples(train_loader,params,sigma,static_params)
- best_beat = np.argmin(losses)
- worst_beat = np.argmax(losses)
-
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[best_beat],params['mus'],sigma)
+ gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[0],params['mus'],sigma)
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
resampled_gaussians = proc.normalize(resampled_gaussians)
- plt.figure()
- plt.plot(t_base_orig, data[best_beat])
- plt.plot(t_base_nyq,dataset_nyq[best_beat],"d")
- plt.plot(t_base_orig,gaussian_lvl_crossing_data)
- plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
- plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./best_loss:{np.min(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
- plt.close()
-
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[worst_beat],params['mus'],sigma)
- gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
- resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
- resampled_gaussians = proc.normalize(resampled_gaussians)
plt.figure()
- plt.plot(t_base_orig, data[worst_beat])
- plt.plot(t_base_nyq,dataset_nyq[worst_beat],"d")
+ plt.plot(t_base_orig, data[0])
+ plt.plot(t_base_nyq,dataset_nyq[0],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./worst_loss:{np.max(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
+ plt.savefig(f'{res_folder_this_run}/unbatched_loss:{loss}_sigma:{sigma}.svg')
plt.close()
-
sigma /= SIGMA_EPOCH_DIVISOR
print(f"END OF SIGMA EPOCH {i+1}, LOSS = {loss}")
print(params)
+ plt.figure()
plt.plot(loss_sigmas)
- plt.show()
+ plt.savefig(f'{res_folder_this_run}/lossVSepoch.svg')
+ plt.close()
- pt = 3
+ pt = 0
#create gaussain level crossing:
mu_s = params['mus']
gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[pt],mu_s,sigma)
gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
resampled_gaussians = proc.normalize(resampled_gaussians)
#Testing grounds
plt.plot(t_base_orig, data[pt])
plt.plot(t_base_nyq,dataset_nyq[pt],"d")
plt.plot(t_base_orig,gaussian_lvl_crossing_data)
plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
plt.show()
if __name__ == "__main__":
#test_signal()
main()
diff --git a/src/integral_test.py b/src/integral_test.py
deleted file mode 100644
index 596ec8e..0000000
--- a/src/integral_test.py
+++ /dev/null
@@ -1,193 +0,0 @@
-from typing import Dict, List, Set
-import numpy as np
-import matplotlib.pyplot as plt
-import numpy.fft as fft
-from copy import copy
-from tqdm import tqdm
-import pandas as pd
-
-
-def sinc_interp(support,function,delta_new_support):
- delta_current_support = support[1]-support[0]
- new_support = np.arange(min(support),max(support)+delta_new_support,delta_new_support)
- new_function = np.zeros_like(new_support)
- print(len(new_support))
- for i,s in tqdm(enumerate(new_support)):
- val = 0
- for n,f in enumerate(function):
- val += f*np.sinc((s-n*delta_current_support)/delta_current_support)
- new_function[i] = val
- return new_support,new_function
-
-
-
-def load_signal():
- df = pd.read_csv("../ecg_syn/ecgsyn.dat",header=0,names=['t','v','i'],sep = " ")
- t = df['t'].to_numpy()
- v = df['v'].to_numpy()
- v -= min(v)+0.01
- return t,v
-
-
-def create_signal(coefs, ws, dt, t_span):
- t = np.linspace(0,t_span,int(t_span/dt))
- x = np.zeros(len(t))
- for w,c in tqdm(zip(ws, coefs)):
- x += c*np.sin(w*t)
- return t,x
-
-def create_pos_signal(coefs, ws, dt, t_span):
- t,x = create_signal(coefs, ws, dt, t_span)
- x -= min(x)+0.01
- return t,x
-
-def get_signal(select = None, coefs = None, ws = None, dt = None, t_span = None):
- if select == 0:
- t,v = create_pos_signal(coefs, ws, dt, t_span)
- else:
- t,v = load_signal()
- return t,v
-
-def integrate(delta,signal):
- integral_vals = [signal[0]]
- for v in signal[1:]:
- integral_vals.append(integral_vals[-1]+v*delta)
- return integral_vals
-
-def get_max_freq(signal, dt, t_base, stopping_prec = 100, v = False):
- signal_0_mean = signal-np.mean(signal)
- f_sig = fft.fft(signal_0_mean)[0:len(signal_0_mean)//2]
- freq_sig = np.fft.fftfreq(t_base.size, d=dt)[0:len(signal_0_mean)//2]
-
- threshold = max(abs(f_sig))/stopping_prec
- threshold_eng = sum(abs(f_sig)**2)/stopping_prec
-
- eng_thr = False
- last_significant_coef_pos = 0
- magn_f_c = 0
- for i,f_c in enumerate(f_sig[-1::-1]):
- if eng_thr:
- magn_f_c += abs(f_c)**2
- if magn_f_c > threshold_eng:
- last_significant_coef_pos = len(f_sig)-1-i
- break
- else:
- magn_f_c = abs(f_c)
- if magn_f_c > threshold:
- last_significant_coef_pos = len(f_sig)-1-i
- break
- max_freq = freq_sig[last_significant_coef_pos]
-
- if v:
- print("\tf max: {} (value: {}, threshold: {} --> delta: {})"
- .format(freq_sig[last_significant_coef_pos],abs(f_sig[last_significant_coef_pos]), threshold, abs(f_sig[last_significant_coef_pos])-threshold
- )
- )
- plt.figure()
- plt.plot(freq_sig,abs(f_sig))
- plt.show()
-
- if max_freq > 10000 and None:
- print("\tf max: {} (value: {}, threshold: {} --> delta: {})"
- .format(freq_sig[last_significant_coef_pos],abs(f_sig[last_significant_coef_pos]), threshold, abs(f_sig[last_significant_coef_pos])-threshold
- )
- )
- plt.figure()
- plt.plot(freq_sig,abs(f_sig),"d-")
- plt.figure()
- plt.plot(t_base,signal_0_mean,"d-")
- plt.show()
-
- return max_freq
-
-def invert_fun(t,v):
- v_base = np.linspace(min(v),max(v),len(t))
- t_resamp = np.interp(v_base,v,t)
- dv = abs(v_base[1]-v_base[0])
- return v_base,t_resamp,dv
-
-
-def sample_signal_at_freq(signal, support, freq):
- support_s = np.arange(support[0],support[-1]+1/freq,1/freq)
- signal_s = np.interp(support_s,support,signal)
- return support_s,signal_s
-
-
-def main():
- rng = np.random.default_rng(31415926514)
- w_orig = rng.choice(100, size=10, replace=False)
- coefs = rng.choice(3000, size=10)
- BW = max(w_orig)
- f_m = BW/(np.pi*2)
- t_span = 10
- dt_orig = 1/100#1/(1000*2.1*f_m)
-
- print("Creating original signal")
- t_orig,x_orig = get_signal(1,coefs, w_orig,dt_orig,t_span)
- #plt.figure()
- #plt.plot(t_orig,x_orig)
- #plt.show()
-
- print("Analyzing original signal")
- f__max_numerical = get_max_freq(x_orig,dt_orig,t_orig, v = True)
- print("Max freq: ",sorted(w_orig/(2*np.pi))[-1], "Max numerical freq: ",f__max_numerical)
-
- print("Sampling original signal")
- f_s = 2.1*f__max_numerical
- t_s,x_s = sample_signal_at_freq(x_orig, t_orig, f_s)
-
- # -------------------------------- INTEGRAL SIGNAL
- print("\n-------------------------")
- print("Integrating signal")
- x_integral = integrate(dt_orig,x_orig)
- t_integral = t_orig
-
- # -------------------------------- INVERTED INTEGRAL SIGNAL
- print("\n-------------------------")
- print("Inverting signal")
- x_inv,t_inv,dv = invert_fun(t_integral,x_integral)
-
- # -------------------------------- INVERTED DIFFERENTIAL INTEGRAL SIGNAL
- print("\n-------------------------")
- print("Differentiating the integrated inverted signal")
- t_inv_grad = np.gradient(t_inv)
-
- print("Analyzing differential inverted integral signal")
- f__diff_inv_max_numerical_integral = get_max_freq(t_inv_grad,dv,x_inv, v = True)
- print("Max numerical freq: ",f__diff_inv_max_numerical_integral)
-
- print("Sampling differential inverted integral signal")
- f_diff_inv_s_integral = 2.1*f__diff_inv_max_numerical_integral
- x_diff_inv_s_integral,t_diff_inv_s_integral = sample_signal_at_freq(t_inv_grad, x_inv, f_diff_inv_s_integral)
-
- print(len(x_s),len(x_diff_inv_s_integral),len(x_diff_inv_s_integral)/len(x_s))
-
- #plt.figure()
- #plt.plot(t_integral,x_integral)
- #plt.plot(t_s_integral,x_s_integral,"-d")
- #plt.plot(t_inv_s_integral,x_inv_s_integral,"-o")
- #plt.show()
- plt.figure()
- plt.plot(x_inv,t_inv_grad)
- plt.plot(x_diff_inv_s_integral,t_diff_inv_s_integral)
- plt.show()
-
- print("Reconstructing int-inv-grad")
- x_inv_grad_recon,t_inv_grad_recon = sinc_interp(x_diff_inv_s_integral,t_diff_inv_s_integral,x_inv[1]-x_inv[0])
- plt.figure()
- plt.plot(x_inv,t_inv_grad)
- plt.plot(x_diff_inv_s_integral,t_diff_inv_s_integral,"d")
- plt.plot(x_inv_grad_recon,t_inv_grad_recon)
- plt.show()
-
-
-
-def test_signal():
- t,v = get_signal()
- plt.plot(t,v)
- plt.show()
-
-if __name__ == "__main__":
- #test_signal()
- main()
-
diff --git a/src/jax_test.py b/src/jax_test.py
deleted file mode 100644
index eef5f8e..0000000
--- a/src/jax_test.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# JAX's syntax is (for the most part) same as NumPy's!
-# There is also a SciPy API support (jax.scipy)
-import jax.numpy as jnp
-import matplotlib.pyplot as plt
-import numpy as np
-# JAX's low level API
-# (lax is just an anagram for XLA, not completely sure how they came up with name JAX)
-# Special transform functions (we'll understand what these are very soon!)
-from jax import grad, jit, lax, make_jaxpr, pmap, random, vmap
-from jax import jacfwd, jacrev
-
-
-# Simple helper visualization function
-def visualize_fn(fn, l=-10, r=10, n=10000, new_fig = True, show = True):
- x = np.linspace(l, r, num=n)
- y = [fn(x_1) for x_1 in x]
- if new_fig:
- plt.figure()
- plt.plot(x, y, "-")
- else:
- plt.plot(x, y, "-")
- if show:
- plt.show()
-
-# First example (automatic diff)
-
-def sum_logistic(x): # or maybe a simpler example: sum of squares? (jnp.sum(x**2))
- return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))
-
-x = jnp.arange(3.)
-loss = sum_logistic # rename it to give it some semantics
-
-# By default grad calculates the derivative of a fn w.r.t. 1st parameter!
-# Here we bundled inputs into a 1st param so it doesn't matter.
-grad_loss = grad(loss)
-
-
-#second example
-x = 1. # example input
-
-f = lambda x: x**2 + x + 4 # simple 2nd order polynomial fn
-
-dfdx = grad(f) # 2*x + 1
-d2fdx = grad(dfdx) # 2
-d3fdx = grad(d2fdx) # 0
-
-
-
-
-# Edge case |x|, how does JAX handle it?
-
-f = lambda x: abs(x)
-#visualize_fn(f)
-
-print(f(-1), f(1))
-dfdx = grad(f)
-eps = 1.18e-38
-print(dfdx(-eps)) # play with numbers around 0 and see how JAX handles it
-print(dfdx(eps)) # play with numbers around 0 and see how JAX handles it
-#visualize_fn(dfdx)
-x = np.random.randn(3, 4)
-y = np.random.randn(4)
-print(make_jaxpr(f)(x))
-
-def pure_uses_internal_state(x):
-
- state = dict(even=0, odd=0)
- for i in range(10):
- state['even' if i % 2 == 0 else 'odd'] += x
-
- return state['even'] + state['odd']
-
-@jit
-def pippo(x):
- a = 0.99
- b = 0.8
- k = np.arange(0,100000,1)
- return jnp.sum(jnp.power(a,k)*jnp.cos(jnp.power(b,k)*jnp.pi*x))
-
-print(jit(pure_uses_internal_state)(5.))
-#visualize_fn(pippo, new_fig= True, show=False)
-#visualize_fn(jit(grad(pippo)), new_fig=False, show= True)
-print(make_jaxpr(grad(pippo))(np.float32(1)))
-
-
-
-
-
-
-
-
diff --git a/src/gaussain_collpsing.py b/src/lvl_selecting.py
similarity index 50%
copy from src/gaussain_collpsing.py
copy to src/lvl_selecting.py
index 2cc7c8b..fdd7c86 100644
--- a/src/gaussain_collpsing.py
+++ b/src/lvl_selecting.py
@@ -1,252 +1,221 @@
from functools import partial
import os
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from jax import grad, jit, pmap, value_and_grad, vmap
from jax.scipy.special import logsumexp
from torch.utils.data import DataLoader
import optax
import dataset_generator as dataLoader
import processing as proc
-FREQ = 20
-NUM_SAMPLES = 5_000
-BATCH_SIZE = 1_000
+FREQ = 100
+NUM_SAMPLES = 5_0
+BATCH_SIZE = 1_0
USE_ENERGY_MAX_FREQ_EVAL = True
STOPPING_PERC = 1000
INTERPOLATOR = 'sinc'
SIGNAL_TYPE = 'create' #'create': create a new emyulated ECG signal, 'load': load previous ECG signal, 'random': random sinusoidal mix signal
EPOCHS = 6
-SIGMA_EPOCH = 6
-SIGMA_INIT_DIVISOR = 15
-SIGMA_EPOCH_DIVISOR = 1.7
INIT_FREQ_DIVISOR = 3
NUM_LVLS = 4
LR = 0.01
DEV_MODE = True
def init_pipeline(gaussian_numbers,range_min,range_max,freq):
mu_s = jnp.linspace(range_min+(range_max-range_min)/16,range_max-(range_max-range_min)/16,gaussian_numbers)
params = {'mus':mu_s,'freq':freq}
return params
-def proc_pipeline(params,x,sigma,static_params):
+def proc_pipeline(params,x,static_params):
signal = x
- signal = proc.mix_gaussian_lvl_crossing(signal,params['mus'],sigma)
+ signal = proc.lvl_crossing_stupid(signal,params['mus'])
signal = proc.normalize(signal)
signal = proc.sinc_interpolation_freq_parametrized(signal,params['freq'],FREQ,static_params['time_base'])
signal = proc.normalize(signal)
return signal
-batched_proc_pipeline = pmap(proc_pipeline,in_axes=(None,0,None,None),static_broadcasted_argnums=[3])
-batched_RMSE = pmap(proc.RMSE)
+def loss_one_sample(params,x,obj,static_params):
+ proc_res = proc_pipeline(params,x,static_params)
+ return proc.RMSE(proc_res,obj)
-def batched_loss_fn(params, data, nyquist_sampled_data, sigma, static_params):
- proc_results = batched_proc_pipeline(params, data,sigma, static_params)
+batched_proc_pipeline = vmap(proc_pipeline,in_axes=(None,0,None))
+batched_RMSE = vmap(proc.RMSE)
+
+def batched_loss_fn(params, data, nyquist_sampled_data, static_params):
+ proc_results = batched_proc_pipeline(params, data, static_params)
return jnp.mean(batched_RMSE(proc_results,nyquist_sampled_data))
-def compute_loss(dataset,params,sigma,static_params):
+def compute_loss(dataset,params,static_params):
loss = 0
i = 0
for (batch,objective_batch) in dataset:
i+=1
- loss += batched_loss_fn(params, batch, objective_batch,sigma, static_params)
+ loss += batched_loss_fn(params, batch, objective_batch, static_params)
return loss/i
-def train(train_dataset,params,sigma,static_params, lr=LR):
- print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
+def train(train_dataset,params,static_params, lr=LR):
+ print(f"Loss: {compute_loss(train_dataset,params,static_params)}")
optimizer = optax.adam(lr)
opt_state = optimizer.init(params)
for e in range(EPOCHS):
print(f"Epoch {e+1}")
for (train_batch,objective_batch) in train_dataset:
- grads = grad(batched_loss_fn)(params, train_batch, objective_batch, sigma, static_params)
+ grads = grad(batched_loss_fn)(params, train_batch, objective_batch,static_params)
updates, opt_state = optimizer.update(grads, opt_state)
params = optax.apply_updates(params, updates)
- print(f"Loss: {compute_loss(train_dataset,params,sigma,static_params)}")
+ print(f"Loss: {compute_loss(train_dataset,params,static_params)}")
return params #Final loss
-def compute_loss_each_samples(dataset,params,sigma,static_params): #NO
+def compute_loss_each_samples(dataset,params,static_params): #NO
loss = []
for (batch,objective_batch) in dataset:
- proc_results = batched_proc_pipeline(params, batch,sigma, static_params)
+ proc_results = batched_proc_pipeline(params, batch, static_params)
loss.extend(batched_RMSE(proc_results, objective_batch))
return loss
-def compute_transform_each_samples(train_dataset,params,sigma,static_params, lr=LR):
+def compute_transform_each_samples(train_dataset,params,static_params, lr=LR):
proc_results = []
for (train_batch,_) in train_dataset:
- proc_results.extend(batched_proc_pipeline(params, train_batch,sigma, static_params))
+ proc_results.extend(batched_proc_pipeline(params, train_batch, static_params))
return proc_results #Final loss
def custom_collate_fn(batch):
transposed_data = list(zip(*batch))
data = np.array(transposed_data[0])
obj = np.array(transposed_data[1])
return data, obj
class hashabledict(dict):
def __hash__(self):
return hash(tuple(sorted(self.items())))
class hashable_np_array(np.ndarray):
def __hash__(self):
return int(self.mean()*1_000_000_000)
def main():
if DEV_MODE:
freq_desired = int(input("What base frequency do you need your data: "))
mus_num = int(input("How many levels: "))
- sigma_div = int(input("Sigma fraction of the signal range (1/X): "))
nyq_freq_div = int(input("Sync freq in fraction of nyq. freq: "))
data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=1,freq=freq_desired)
print("Computing Nyquist frequency...")
nyq_freq = proc.get_nyquist_freq_dataset(data,freq_desired,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/freq_desired,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/freq_desired,1/freq_desired)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective")
dataset_nyq = proc.interpolate_dataset(data, freq_desired, nyq_freq)
print("Initializing pipelines Parameters")
- sigma = float((jnp.max(data)-jnp.min(data))/sigma_div)
params = init_pipeline(mus_num,np.min(data),np.max(data),nyq_freq/nyq_freq_div)
- print(f"Parameters: {params}\nSigma: {sigma}")
- print("Computing mixed gaussian representation")
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[0],params['mus'],sigma)
+ print("Computing lvl_cross representation")
+ lvl_crossing_val = proc.lvl_crossing_stupid(data[0],params['mus'])
print("Normalizing")
- gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
+ lvl_crossing_val = proc.normalize(lvl_crossing_val)
print("Computing sync filter")
- resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],freq_desired,t_base_orig)
+ resamp_lvl_crossing = proc.sinc_interpolation_freq_parametrized(lvl_crossing_val,params['freq'],freq_desired,t_base_orig)
print("Normalizing")
- resampled_gaussians = proc.normalize(resampled_gaussians)
+ resamp_lvl_crossing = proc.normalize(resamp_lvl_crossing)
print("Plotting")
plt.figure()
plt.plot(t_base_orig, data[0])
plt.plot(t_base_nyq,dataset_nyq[0],"d")
- plt.plot(t_base_orig,gaussian_lvl_crossing_data)
- plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
+ plt.plot(t_base_orig[:len(lvl_crossing_val)],lvl_crossing_val)
+ plt.plot(t_base_orig,resamp_lvl_crossing,"-")
plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
plt.show()
return
#load/generate data
print("Loading data")
- os.environ["XLA_FLAGS"] = f'--xla_force_host_platform_device_count={BATCH_SIZE}'
+ #os.environ["XLA_FLAGS"] = f'--xla_force_host_platform_device_count={BATCH_SIZE}'
data = dataLoader.get_signal(SIGNAL_TYPE,num_pts=NUM_SAMPLES,freq=FREQ)
if data is None:
print("Failed loading data")
return
data = proc.normalize_dataset(data)
print("\n-------------------\n")
print(f"Loaded Data:shape {data.shape}")
#reference
print("Computing maximum Nyquist frequency for the Dataset...")
nyq_freq = proc.get_nyquist_freq_dataset(data,FREQ,USE_ENERGY_MAX_FREQ_EVAL,STOPPING_PERC)
t_base_nyq = np.arange(0,len(data[0])/FREQ,1/nyq_freq)
t_base_orig = np.arange(0,(len(data[0]))/FREQ,1/FREQ)
print(f"Nyquist frequency: {nyq_freq}")
print("Generating Nyquist sampled objective dataset")
dataset_nyq = proc.interpolate_dataset(data, FREQ, nyq_freq)
train_dataset = [[d,o] for d,o in zip(data,dataset_nyq)]
static_params = hashabledict({'freq' : None,'time_base' : None})
static_params['freq'] = nyq_freq
static_params['time_base'] = t_base_nyq.view(hashable_np_array)
#static_params = t_base_nyq.view(hashable_np_array)
#INIT
print("Initializing pipelines Parameters")
- sigma = float((jnp.max(data)-jnp.min(data))/SIGMA_INIT_DIVISOR)
params = init_pipeline(NUM_LVLS,np.min(data),np.max(data),nyq_freq/INIT_FREQ_DIVISOR)
train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, collate_fn=custom_collate_fn, drop_last=False)
print(f"Parameters: {params}")
+ params = train(train_loader,params,static_params)
+ loss = compute_loss(train_loader,params,static_params)
+
+ losses = compute_loss_each_samples(train_loader,params,static_params)
+ best_beat = np.argmin(losses)
+ worst_beat = np.argmax(losses)
+
+
+ lvl_crossing_val = proc.lvl_crossing_stupid(data[best_beat],params['mus'])
+ lvl_crossing_val = proc.normalize(lvl_crossing_val)
+ resamp_lvl_crossing = proc.sinc_interpolation_freq_parametrized(lvl_crossing_val,params['freq'],FREQ,t_base_orig)
+ resamp_lvl_crossing = proc.normalize(resamp_lvl_crossing)
+ plt.figure()
+ plt.plot(t_base_orig, data[best_beat])
+ plt.plot(t_base_nyq,dataset_nyq[best_beat],"d")
+ plt.plot(t_base_orig[:len(lvl_crossing_val)],lvl_crossing_val)
+ plt.plot(t_base_orig,resamp_lvl_crossing,"-")
+ plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
+ plt.savefig(f'./best_loss:{np.min(losses)}_lvl_cross_time:{os.times().elapsed}.svg')
+ plt.close()
+
+
+ lvl_crossing_val = proc.lvl_crossing_stupid(data[worst_beat],params['mus'])
+ lvl_crossing_val = proc.normalize(lvl_crossing_val)
+ resamp_lvl_crossing = proc.sinc_interpolation_freq_parametrized(lvl_crossing_val,params['freq'],FREQ,t_base_orig)
+ resamp_lvl_crossing = proc.normalize(resamp_lvl_crossing)
+ plt.figure()
+ plt.plot(t_base_orig, data[worst_beat])
+ plt.plot(t_base_nyq,dataset_nyq[worst_beat],"d")
+ plt.plot(t_base_orig,lvl_crossing_val)
+ plt.plot(t_base_orig,resamp_lvl_crossing[:len(t_base_orig)],"-")
+ plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
+ plt.savefig(f'./worst_loss:{np.max(losses)}_lvl_cross_time:{os.times().elapsed}.svg')
+ plt.close()
+
+ print(params)
- loss_sigmas = []
- for i in range(SIGMA_EPOCH):
- print(f"SIGMA EPOCH: {i+1}/{SIGMA_EPOCH}, sigma: {sigma}")
-
-
- params = train(train_loader,params,sigma,static_params)
- loss = compute_loss(train_loader,params,sigma,static_params)
- loss_sigmas.append(loss)
-
- losses = compute_loss_each_samples(train_loader,params,sigma,static_params)
- best_beat = np.argmin(losses)
- worst_beat = np.argmax(losses)
-
-
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[best_beat],params['mus'],sigma)
- gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
- resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
- resampled_gaussians = proc.normalize(resampled_gaussians)
- plt.figure()
- plt.plot(t_base_orig, data[best_beat])
- plt.plot(t_base_nyq,dataset_nyq[best_beat],"d")
- plt.plot(t_base_orig,gaussian_lvl_crossing_data)
- plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
- plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./best_loss:{np.min(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
- plt.close()
-
-
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[worst_beat],params['mus'],sigma)
- gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
- resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
- resampled_gaussians = proc.normalize(resampled_gaussians)
- plt.figure()
- plt.plot(t_base_orig, data[worst_beat])
- plt.plot(t_base_nyq,dataset_nyq[worst_beat],"d")
- plt.plot(t_base_orig,gaussian_lvl_crossing_data)
- plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
- plt.hlines(params['mus'],t_base_orig[0],t_base_orig[-1])
- plt.savefig(f'./worst_loss:{np.max(losses)}_sigma:{sigma}_time:{os.times().elapsed}.svg')
- plt.close()
-
-
- sigma /= SIGMA_EPOCH_DIVISOR
- print(f"END OF SIGMA EPOCH {i+1}, LOSS = {loss}")
- print(params)
- plt.plot(loss_sigmas)
- plt.show()
-
-
- pt = 3
- #create gaussain level crossing:
- mu_s = params['mus']
- gaussian_lvl_crossing_data = proc.mix_gaussian_lvl_crossing(data[pt],mu_s,sigma)
- gaussian_lvl_crossing_data = proc.normalize(gaussian_lvl_crossing_data)
- resampled_gaussians = proc.sinc_interpolation_freq_parametrized(gaussian_lvl_crossing_data,params['freq'],FREQ,t_base_orig)
- resampled_gaussians = proc.normalize(resampled_gaussians)
-
- #Testing grounds
-
- plt.plot(t_base_orig, data[pt])
- plt.plot(t_base_nyq,dataset_nyq[pt],"d")
- plt.plot(t_base_orig,gaussian_lvl_crossing_data)
- plt.plot(t_base_orig,resampled_gaussians[:len(t_base_orig)],"-")
- plt.show()
if __name__ == "__main__":
#test_signal()
main()
diff --git a/src/processing.py b/src/processing.py
index ab38c41..1850ccb 100644
--- a/src/processing.py
+++ b/src/processing.py
@@ -1,96 +1,108 @@
import numpy as np
import numpy.fft as fft
import jax.numpy as jnp
+from jax import lax
INTERPOLATOR = 'sinc'
SIGNAL_TYPE = 'create' #'create': create a new emyulated ECG signal, 'load': load previous ECG signal, 'random': random sinusoidal mix signal
def get_max_freq(signal, freq, use_energy, stopping_prec):
dt = 1/freq
signal_0_mean = signal-np.mean(signal)
f_sig = fft.fft(signal_0_mean)[0:len(signal_0_mean)//2]
freq_sig = np.fft.fftfreq(signal.size, d=dt)[0:len(signal_0_mean)//2]
if use_energy:
threshold = sum(abs(f_sig)**2)/stopping_prec
eval_coeff = lambda x: abs(x)**2
else:
threshold = max(abs(f_sig))/stopping_prec
eval_coeff = lambda x: abs(x)
last_significant_coef_pos = 0
magn_f_c = 0
for i,f_c in enumerate(f_sig[-1::-1]):
magn_f_c += eval_coeff(f_c)
if magn_f_c > threshold:
last_significant_coef_pos = len(f_sig)-1-i
break
max_freq = freq_sig[last_significant_coef_pos]
return max_freq
def get_nyquist_freq_dataset(data,freq,use_energy,stopping_perc):
max_f = 0
for pt in data:
f = get_max_freq(pt,freq,use_energy,stopping_perc)
max_f = max(max_f,f)
return 2.1*max_f
def normalize(signal):
return signal/(jnp.max(signal)-jnp.min(signal))
def normalize_dataset(data):
out =[]
for pt in data:
out.append(normalize(pt))
return jnp.array(out)
# ----------------------------------
def gaussian(x, mu, sig):
return jnp.exp(-jnp.power(x - mu, 2.) / (2 * jnp.power(sig, 2.)))
def mix_gaussian_lvl_crossing(signal,mu_s,sigma):
return signal*jnp.sum(jnp.array([gaussian(signal,mu,sigma) for mu in mu_s]),axis = 0)
+def lvl_crossing_stupid(x,mus):
+ out = jnp.zeros((len(x)-1,))
+ for i in range(1,len(x)):
+ for mu in mus:
+ #val = lax.cond((x[i-1]<mu and x[i]>=mu) or (x[i-1]>mu and x[i]<=mu),x[i],val)
+ if (x[i-1]<mu and x[i]>=mu) or (x[i-1]>mu and x[i]<=mu):
+ out = out.at[i].set(mu) #GRAD IS 0 IF I USE X[I] BUT NOT IF I USE MU
+ return out
+
def mix_gaussian_lvl_crossing_dataset(data,mu_s,sigma):
out = []
for signal in data:
out.append(mix_gaussian_lvl_crossing(signal,mu_s,sigma))
return np.array(out)
def sinc_interpolation_freq_parametrized(samples,freq,freq_in,time_base):
dt_samples = 1/freq_in
sinc_matr = jnp.array([jnp.sinc((time_base-n*dt_samples)*freq) for n in range(len(samples))])
return jnp.dot(samples,sinc_matr)
+ '''
out = samples[0]*jnp.sinc((time_base-0*dt_samples)*freq)
for n,f in enumerate(samples[1:]):
out += f*jnp.sinc((time_base-n*dt_samples)*freq) #interpolation with sync frequyency parametrized
return out
+ '''
def RMSE(v1,v2):
- return jnp.sqrt(np.sum((v1-v2)**2)/len(v1))
+ return jnp.sqrt(jnp.sum((v1-v2)**2)/len(v1))
# ----------------------------------
def sinc_interp(samples,freq_in,freq_out):
dt_samples = 1/freq_in
dt_new = 1/freq_out
new_time_base = np.arange(0,len(samples)*dt_samples,dt_new)
out = np.zeros((len(new_time_base)))
for n,f in enumerate(samples):
out += f*np.sinc((new_time_base-n*dt_samples)/dt_samples) #Whitaker Shannon
return out
def interpolate(signal,freq_in,freq_out,type = INTERPOLATOR):
if type == "sinc":
resampled = sinc_interp(signal,freq_in,freq_out)
else:
print("No interpolator recognized")
resampled = signal
return resampled
def interpolate_dataset(data,freq_in,freq_out,type = INTERPOLATOR):
out = []
for pt in data:
out.append(interpolate(pt,freq_in,freq_out))
return np.array(out)
\ No newline at end of file
diff --git a/src/worst_loss:0.16785910725593567_sigma:0.039215688319767225_time:17222522.53.svg b/src/worst_loss:0.16785910725593567_sigma:0.039215688319767225_time:17222522.53.svg
deleted file mode 100644
index 2446a56..0000000
--- a/src/worst_loss:0.16785910725593567_sigma:0.039215688319767225_time:17222522.53.svg
+++ /dev/null
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