def plot(out,labels,name):
# plot
img_size = 15
fig, ax = plt.subplots()
im, cbar = ahm.heatmap(out, labels, labels, ax=ax,
cmap="gist_heat", cbarlabel='Jaccard Index')
ahm.annotate_heatmap(im, valfmt="{x:.1f}", fontsize = 10)
fig.tight_layout()
fig = plt.gcf()
fig.set_size_inches(img_size, img_size)
# plt.show()
fig.savefig('heatmaps/'+name+'.png', dpi=100, transparent = True)
def plot(out, labels, name):
# plot
img_size = 15
fig, ax = plt.subplots()
im, cbar = ahm.heatmap(out,
labels,
labels,
ax=ax,
cmap="gist_heat",
cbarlabel='Jaccard Index')
ahm.annotate_heatmap(im, valfmt="{x:.1f}", fontsize=10)
fig.tight_layout()
fig = plt.gcf()
fig.set_size_inches(img_size, img_size)
plt.show()
def output_csv_heatmap(corpus_load_path,
ngram,
second_order,
ignore_frequency,
csv_save_path,
heatmap_save_path,
inds,
img_size=10,
font_size=20,
annotate=False):
# load test corpus
wd, mydict = loadCorpus(corpus_load_path)
# get first order matrix
f = jac.get_jaccard_matrix(wd, ngram, second_order, ignore_frequency)
np.fill_diagonal(f, 0)
out = pd.DataFrame(f)
out.columns = list(mydict.keys())
out.index = list(mydict.keys())
out.to_csv(csv_save_path, index=False)
# plot first order
fig, ax = plt.subplots()
im, cbar = ahm.heatmap(f[:inds, :inds],
list(mydict.keys())[:inds],
list(mydict.keys())[:inds],
ax=ax,
cmap="gist_heat",
cbarlabel='Jaccard Index')
if annotate:
ahm.annotate_heatmap(im, valfmt="{x:.2f}", fontsize=font_size)
fig.tight_layout()
fig = plt.gcf()
fig.set_size_inches(img_size, img_size)
#plt.show()
fig.savefig(heatmap_save_path, dpi=100, transparent=True)
vect = np.ones(dims, 'complex128')
for i in sparse.find(row)[1]:
vect *= np.fft.fft(vects[i])
for i in sparse.find(row)[1]:
mem_conv[i] += vect
mem_conv = np.fft.irfft(mem_conv)
first_conv = cosine_table_self(mem_conv)
np.fill_diagonal(first_conv, 0)
img_size = 15
fig, ax = plt.subplots()
im, cbar = ahm.heatmap(first_conv,
sorted(mydict.keys()),
sorted(mydict.keys()),
ax=ax,
cmap="gist_heat",
cbarlabel='Jaccard Index')
ahm.annotate_heatmap(im, valfmt="{x:.1f}", fontsize=10)
fig.tight_layout()
fig = plt.gcf()
fig.set_size_inches(img_size, img_size)
second_conv = cosine_table_self(first_conv)
np.fill_diagonal(second_conv, 0)
img_size = 15
fig, ax = plt.subplots()
im, cbar = ahm.heatmap(second_conv,
sorted(mydict.keys()),
sorted(mydict.keys()),