def run(self, plot_flag=1):
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
if self.custom_labels:
labels = self.labels
mat = np.array(mat, dtype=np.float64)
seed = np.random.RandomState(seed=2)
X_embedded = TSNE(n_components=2,
random_state=seed,
metric='precomputed').fit_transform(mat)
# X_embedded = SNE_RAW().fit(mat)
if plot_flag == 1:
plt = fun.plot(labels, X_embedded)
plt.savefig(self.path_to_results + 't-sne.png')
print('Error TSNE 2D: ', str(er.error(mat, X_embedded)) + '%')
seed3d = np.random.RandomState(seed=5)
embedding3d = TSNE(n_components=3,
random_state=seed3d,
metric='precomputed')
X_transformed3d = embedding3d.fit_transform(mat)
if plot_flag == 1:
plt3d = fun.plot(labels, X_transformed3d, components=3)
plt3d.savefig(self.path_to_results + 't-sne3D.png')
print('Error TSNE 3D: ',
str(er.error(mat, X_transformed3d, components=3)) + '%')
return X_embedded
def run(self, plot_flag=1):
seed = np.random.RandomState(seed=2)
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
mat = np.array(mat, dtype=np.float64)
#best=1
#minErr=10000
#for i in range (1,92):
# embedding = Isomap(n_neighbors=i, n_components=2, metric='precomputed')
# X_transformed = embedding.fit_transform(mat)
# erAct=er.error(mat, X_transformed)
#if (erAct<minErr):
# best=i
# minErr=erAct
#print(best)
embedding = Isomap(n_neighbors=29,
n_components=2,
metric='precomputed')
X_transformed = embedding.fit_transform(mat)
if plot_flag == 1:
plt = fun.plot(labels, X_transformed)
plt.savefig(self.path_to_results + 'isomap.png')
print('Error ISOMAP 2D: ', str(er.error(mat, X_transformed)) + '%')
embedding3d = Isomap(n_components=3)
X_transformed3d = embedding3d.fit_transform(mat)
if plot_flag == 1:
plt3d = fun.plot(labels, X_transformed3d, components=3)
plt3d.savefig(self.path_to_results + 'isomap3D.png')
print('Error ISOMAP 3D: ',
str(er.error(mat, X_transformed3d, components=3)) + '%')
return X_transformed
def run(self):
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
mat = fun.diag_zeros(mat)
mat = np.array(mat, dtype=np.float64)
seed = np.random.RandomState(seed=3)
seed3d = np.random.RandomState(seed=5)
embedding = MDS(n_components=2,
metric=False,
max_iter=3000,
eps=1e-12,
dissimilarity="precomputed",
random_state=seed,
n_jobs=1,
n_init=1)
X_transformed = embedding.fit_transform(mat)
plt = fun.plot(labels, X_transformed)
plt.savefig(self.path_to_results + 'mds.png')
print('Error: ', str(er.error(mat, X_transformed)) + '%')
embedding3d = MDS(n_components=3,
dissimilarity='precomputed',
random_state=seed3d,
metric=False)
X_transformed3d = embedding3d.fit_transform(mat)
plt3d = fun.plot(labels, X_transformed3d, components=3)
plt3d.savefig(self.path_to_results + 'mds3D.png')
print('Error: ',
str(er.error(mat, X_transformed3d, components=3)) + '%')
def run(self):
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
mat = np.array(mat, dtype=np.float64)
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(mat)
plt = fun.plot(labels, principalComponents)
plt.savefig(self.path_to_results + 'pca.png')
print('Error: ', str(er.error(mat, principalComponents)) + '%')
seed3d = np.random.RandomState(seed=5)
embedding3d = PCA(n_components=3, random_state=seed3d)
X_transformed3d = embedding3d.fit_transform(mat)
plt3d = fun.plot(labels, X_transformed3d, components=3)
plt3d.savefig(self.path_to_results + 'pca3D.png')
print('Error: ',
str(er.error(mat, X_transformed3d, components=3)) + '%')
def run(self, plot_flag=1):
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
mat = np.array(mat, dtype=np.float64)
fun.triangle_inequality(mat)
ti = fun.to_distance_matrix(mat)
fun.triangle_inequality(ti)
#print(ti)
seed = np.random.RandomState(seed=5)
seed3d = np.random.RandomState(seed=4)
embedding = MDS(n_components=2,
dissimilarity='precomputed',
random_state=seed,
metric=True)
X_transformed = embedding.fit_transform(ti)
if plot_flag == 1:
plt = fun.plot(labels, X_transformed)
plt.savefig(self.path_to_results + 'mdsMetric.png')
print('Error MDS Corrected 2D: ',
str(er.error(ti, X_transformed)) + '%')
embedding3d = MDS(n_components=3,
dissimilarity='precomputed',
random_state=seed3d,
metric=False)
X_transformed3d = embedding3d.fit_transform(mat)
if plot_flag == "1":
plt3d = fun.plot(labels, X_transformed3d, components=3)
plt3d.savefig(self.path_to_results + 'mds_raw3D.png')
print('Error MDS Corrected 3D: ',
str(er.error(mat, X_transformed3d, components=3)) + '%')
return X_transformed
def run(self):
mat, labels = fun.readMatrix(self.path_to_file + self.currentFile)
if self.custom_labels:
labels = self.labels
mat = np.array(mat, dtype=np.float64)
seed = np.random.RandomState(seed=self.seed)
# MAGIA
if self.method == 'sne':
X = TSNE(n_components=2, random_state=seed, metric='precomputed').fit_transform(mat)
elif self.method == 'mds':
embedding = MDS(n_components=2, metric=False, max_iter=3000, eps=1e-12,
dissimilarity="precomputed", random_state=seed, n_jobs=1,
n_init=1)
X = embedding.fit_transform(mat)
if self.plot:
plt = fun.plot(labels, X)
# plt.savefig(self.path_to_results + 't-sne.png')
print('Error: ', str(er.error(mat, X)) + '%')
return X