update

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/_build/html/.buildinfo

@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: fca715a87e6687f8584f71a09f20b6ce
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_build/html/_sources/_example/muller-example.rst.txt

@@ -0,0 +1,367 @@
+.. 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
+