.. code:: ipython3

    import numpy as np
    import torch
    import torch.nn as nn
    from matplotlib import pyplot as plt
    
    from torch.utils.data.dataloader import DataLoader
    from torch.utils.data import random_split
    
    from tsdart.utils import set_random_seed
    from tsdart.loss import Prototypes
    from tsdart.model import TSDART, TSDARTLayer, TSDARTEstimator
    from tsdart.dataprocessing import Preprocessing

.. code:: ipython3

    if torch.cuda.is_available():
        device = torch.device('cuda')
        print('cuda is available')
    else:
        device = torch.device('cpu')
        print('cpu')


.. parsed-literal::

    cpu
    

Muller potential
~~~~~~~~~~~~~~~~

.. code:: ipython3

    A = np.array([-10,-5,-17/2,0.75])
    a = np.array([-1,-1,-6.5,0.7])
    b = np.array([0,0,11,0.6])
    c = np.array([-10,-10,-6.5,0.7])
    xbar = np.array([1,0,-0.5,-1])
    ybar = np.array([0,0.5,1.5,1])
    
    def V(x,y):
        s = 0.
        for i in range(4):
            s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) 
        return s
    
    fig,ax = plt.subplots(1,1,figsize=(4,3))
    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(2)
    ax.set_aspect('equal')
    x = np.arange(-1.7,1.2+0.01,0.01)
    y = np.arange(-0.35,2.1+0.01,0.01)
    xx,yy = np.meshgrid(x,y)
    z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
    z = z - z.min()
    z = z*1/0.9 # temperature is 0.4.
    z = np.ma.masked_greater(z, 10)
    c = ax.contourf(x,y,z,cmap='rainbow',levels=20,zorder=1)
    ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=0.2)
    cb = fig.colorbar(c)
    #ax.grid(True)
    ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
    cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
    cb.set_label('free energy/kT',fontsize=12)
    
    ax.set_xlim(-1.5,1.15)
    ax.set_ylim(-0.3,2.1)
    
    ax.set_yticks([0,1,2])
    
    r=0.1
    g=0.1
    b=0.2
    ax.patch.set_facecolor((r,g,b,.15))
    
    ax.set_xlabel('x1',fontsize=12)
    ax.set_ylabel('x2',fontsize=12)




.. parsed-literal::

    Text(0, 0.5, 'x2')




.. image:: output_3_1.png


Create dataset
~~~~~~~~~~~~~~

.. code:: ipython3

    data = np.load('../data/muller/muller.npy')
    
    pre = Preprocessing(dtype=np.float32)
    dataset = pre.create_dataset(lag_time=1,data=data)

2 states model
~~~~~~~~~~~~~~

.. code:: ipython3

    set_random_seed(1)
    
    val = int(len(dataset)*0.10)
    train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
    
    loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
    loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
    
    lobe = TSDARTLayer([2,20,20,20,10,2],n_states=2)
    lobe = lobe.to(device=device)
    ### 50 epochs for fully optimization
    tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=2, pretrain=50)
    tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model()


.. parsed-literal::

                                                              


.. code:: ipython3

    tsdart_estimator = TSDARTEstimator(tsdart_model)
    ood_scores = tsdart_estimator.fit(data).ood_scores

.. code:: ipython3

    A = np.array([-10,-5,-17/2,0.75])
    a = np.array([-1,-1,-6.5,0.7])
    b = np.array([0,0,11,0.6])
    c = np.array([-10,-10,-6.5,0.7])
    xbar = np.array([1,0,-0.5,-1])
    ybar = np.array([0,0.5,1.5,1])
    
    def V(x,y):
        s = 0.
        for i in range(4):
            s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) 
        return s
    
    fig,ax = plt.subplots(1,1,figsize=(4,3))
    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(2)
    ax.set_aspect('equal')
    x = np.arange(-1.7,1.2+0.01,0.01)
    y = np.arange(-0.35,2.1+0.01,0.01)
    xx,yy = np.meshgrid(x,y)
    z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
    z = z - z.min()
    z = z*1/0.9 # temperature is 0.4.
    z = np.ma.masked_greater(z, 10)
    
    c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
    cb = fig.colorbar(c)
    cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
    cb.set_label('ood scores',fontsize=12)
    
    ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
    
    ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
    
    ax.set_xlim(-1.5,1.15)
    ax.set_ylim(-0.3,2.1)
    
    ax.set_yticks([0,1,2])
    
    r=0.1
    g=0.1
    b=0.2
    ax.patch.set_facecolor((r,g,b,.15))
    
    ax.set_xlabel('x1',fontsize=12)
    ax.set_ylabel('x2',fontsize=12)




.. parsed-literal::

    Text(0, 0.5, 'x2')




.. image:: output_9_1.png


.. code:: ipython3

    features = tsdart_model.transform(data,return_type='hypersphere_embs')
    state_centers = tsdart_estimator.fit(data).state_centers

.. code:: ipython3

    fig,ax = plt.subplots(1,1,figsize=(4,3))
    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(1)
    ax.set_aspect('equal')
    
    c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
    cb = fig.colorbar(c)
    cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
    cb.set_label('ood scores',fontsize=12)
    
    ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
    ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
    
    ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
    
    ax.set_xlim(-1.1,1.1)
    ax.set_ylim(-1.1,1.1)
    
    ax.set_xticks([-1,0,1])
    ax.set_yticks([-1,0,1])
    
    ax.set_xlabel('z1',fontsize=12)
    ax.set_ylabel('z2',fontsize=12)
    
    r=0.1
    g=0.1
    b=0.2
    ax.patch.set_facecolor((r,g,b,.15))



.. image:: output_11_0.png


3 states model
~~~~~~~~~~~~~~

.. code:: ipython3

    set_random_seed(1)
    
    val = int(len(dataset)*0.10)
    train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
    
    loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
    loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
    
    lobe = TSDARTLayer([2,20,20,20,10,2],n_states=3)
    lobe = lobe.to(device=device)
    ### 50 epochs for fully optimization
    tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=3, pretrain=50)
    tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model()


.. parsed-literal::

                                                              


.. code:: ipython3

    tsdart_estimator = TSDARTEstimator(tsdart_model)
    ood_scores = tsdart_estimator.fit(data).ood_scores

.. code:: ipython3

    A = np.array([-10,-5,-17/2,0.75])
    a = np.array([-1,-1,-6.5,0.7])
    b = np.array([0,0,11,0.6])
    c = np.array([-10,-10,-6.5,0.7])
    xbar = np.array([1,0,-0.5,-1])
    ybar = np.array([0,0.5,1.5,1])
    
    def V(x,y):
        s = 0.
        for i in range(4):
            s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) 
        return s
    
    fig,ax = plt.subplots(1,1,figsize=(4,3))
    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(2)
    ax.set_aspect('equal')
    x = np.arange(-1.7,1.2+0.01,0.01)
    y = np.arange(-0.35,2.1+0.01,0.01)
    xx,yy = np.meshgrid(x,y)
    z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
    z = z - z.min()
    z = z*1/0.9 # temperature is 0.4.
    z = np.ma.masked_greater(z, 10)
    
    c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
    cb = fig.colorbar(c)
    cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
    cb.set_label('ood scores',fontsize=12)
    
    ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
    
    ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
    
    ax.set_xlim(-1.5,1.15)
    ax.set_ylim(-0.3,2.1)
    
    ax.set_yticks([0,1,2])
    
    r=0.1
    g=0.1
    b=0.2
    ax.patch.set_facecolor((r,g,b,.15))
    
    ax.set_xlabel('x1',fontsize=12)
    ax.set_ylabel('x2',fontsize=12)




.. parsed-literal::

    Text(0, 0.5, 'x2')




.. image:: output_15_1.png


.. code:: ipython3

    features = tsdart_model.transform(data,return_type='hypersphere_embs')
    state_centers = tsdart_estimator.fit(data).state_centers

.. code:: ipython3

    fig,ax = plt.subplots(1,1,figsize=(4,3))
    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(1)
    ax.set_aspect('equal')
    
    c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
    cb = fig.colorbar(c)
    cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
    cb.set_label('ood scores',fontsize=12)
    
    ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
    ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
    ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],linewidth=2,color='black',linestyle='--')
    
    ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
    
    ax.set_xlim(-1.1,1.1)
    ax.set_ylim(-1.1,1.1)
    
    ax.set_xticks([-1,0,1])
    ax.set_yticks([-1,0,1])
    
    ax.set_xlabel('z1',fontsize=12)
    ax.set_ylabel('z2',fontsize=12)
    
    r=0.1
    g=0.1
    b=0.2
    ax.patch.set_facecolor((r,g,b,.15))



.. image:: output_17_0.png