# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues')
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Scatter plot of q
# col +=1
ax = plt.subplot2grid((rows, cols), (row, samp_i + 1), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# plot_isocontours(ax, func, cmap='Reds',xlimits=xlimits,ylimits=ylimits)
func = lambda zs: model.logposterior_func(samp_torch, zs)
plot_isocontours2_exp_norm(ax,
func,
cmap='Blues',
legend=legend,
xlimits=xlimits,
ylimits=ylimits)
# if samp_i==0: ax.annotate('p(z|x,W1)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
# #Plot q
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# # # mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
# samp_torch = Variable(torch.from_numpy(np.array([samp]))).type(model.dtype)
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Scatter plot of q
# col +=1
ax = plt.subplot2grid((rows,cols), (row,samp_i+1), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# plot_isocontours(ax, func, cmap='Reds',xlimits=xlimits,ylimits=ylimits)
func = lambda zs: model.logposterior_func(samp_torch,zs)
plot_isocontours2_exp_norm(ax, func, cmap='Blues', legend=legend,xlimits=xlimits,ylimits=ylimits)
# if samp_i==0: ax.annotate('p(z|x,W1)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
# #Plot q
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues')
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Scatter plot of q
# col +=1
ax = plt.subplot2grid((rows, cols), (row, samp_i + 1), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# plot_isocontours(ax, func, cmap='Reds',xlimits=xlimits,ylimits=ylimits)
func = lambda zs: model.logposterior_func(samp_torch, zs)
plot_isocontours2_exp_norm(ax,
func,
cmap='Greens',
legend=legend,
xlimits=xlimits,
ylimits=ylimits)
# if samp_i==0: ax.annotate('p(z|x,W1)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
# #Plot q
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# # # mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
# samp_torch = Variable(torch.from_numpy(np.array([samp]))).type(model.dtype)
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Plot q
col +=1
val = 3
ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
plot_isocontours(ax, func, cmap='Reds', xlimits=[-val, val], ylimits=[-val, val])
if samp_i==0: ax.annotate('p(z)\nq(z|x)\np(z|x)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
plot_isocontours(ax, func, cmap='Blues', alpha=.3, xlimits=[-val, val], ylimits=[-val, val])
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
plot_isocontours2_exp_norm(ax, func, cmap='Greens', legend=legend, xlimits=[-val, val], ylimits=[-val, val])
# #Plot logprior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontoursNoExp(ax, func, cmap='Blues', legend=legend)
# if samp_i==0: ax.annotate('Prior\nlogp(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
# #Plot logq
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# plot_isocontoursNoExp(ax, func, cmap='Reds', legend=legend)
# if samp_i==0: ax.annotate('Post Approx\nlog q(z|x)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# plot_isocontours(ax, func, cmap='Reds')
#Plot prob
col +=1
ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
plot_isocontours(ax, func, cmap='Reds',xlimits=xlimits,ylimits=ylimits)
func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
plot_isocontours2_exp_norm(ax, func, cmap='Greens', legend=legend,xlimits=xlimits,ylimits=ylimits)
if samp_i==0: ax.annotate('p(z|x,W1)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# #Plot prob
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# # func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# # plot_isocontours(ax, func, cmap='Reds')
# # func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
# plot_isocontours2_exp_norm_logspace(ax, func, cmap='Greens', legend=legend)
# if samp_i==0: ax.annotate('p(z|x,W1)', xytext=(.1, 1.1), xy=(0, 1), textcoords='axes fraction')
