def fisher_analysis(X, dg):
X_shuffle = np.zeros(X.shape)
# Shuffle
mu = []
C = []
C2 = []
fisher_info = 0
fisher_info_shuffle = 0
for di, tf in enumerate([dg.stim_table['temporal_frequency'].unique()[0]]):
for ci, on in enumerate(dg.stim_table.orientation.unique()):
unique_index = dg.stim_table[(dg.stim_table.orientation == on) & (
dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(
X[unique_index])
mu = mu + [(X[unique_index]).mean(axis=0)]
print(mu[0].shape)
temp_X = X[unique_index]
temp_X_shuffle = X_shuffle[unique_index]
C = C + [np.cov(temp_X.T)]
C2 = C2 + [np.cov(temp_X_shuffle.T)]
print(fisher_information(mu, C))
fisher_info += fisher_information(mu, C)
fisher_info_shuffle += fisher_information(mu, C2)
return fisher_info, fisher_info_shuffle
def svm_decoder_dsi(X_original,Y_original,fr,dg,dsi):
X_shuffle = np.zeros(X_original.shape)
fr_shuffle = np.zeros(X_original.shape)
Y_shuffle = Y_original.copy()
# Shuffle
for ci, on in enumerate(dg.stim_table.orientation.unique()):
for di, tf in enumerate(dg.stim_table['temporal_frequency'].unique()):
unique_index = dg.stim_table[
(dg.stim_table.orientation == on) & (dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(X_original[unique_index])
fr_shuffle[unique_index]= (fr[unique_index])[ixi,ixj]
number_trials = 100;
number_cells = 50;
number_test = 30;
number_lambd = 3;
number_of_repeats = 30;
range_of_num = range(2,55,1)
shuffle_vector = []
original_vector = []
fr_shuffle_vector = []
fr_vector = []
for bc,nb in enumerate(range_of_num):
ot_sum = 0;
st_sum = 0;
fr_sum = 0;
fr_st_sum = 0;
cell_ind = (np.argsort(dsi.flatten()))[-nb - 1:-1]
for nr in range(number_of_repeats):
print(nb, ':', nr)
trial_ind = np.random.choice(len(Y_original), number_trials, replace=False);
test_ind = np.random.choice(len(Y_original), number_test, replace=False);
X_train = X_original[trial_ind, :][:, cell_ind];
Y_train = Y_original[trial_ind];
print('should be 8: ', len(np.unique(Y_train)))
X_test = X_original[test_ind, :][:, cell_ind];
Y_test = Y_original[test_ind];
X_shuffle_train = X_shuffle[trial_ind, :][:, cell_ind]
Y_shuffle_train = Y_shuffle[trial_ind]
fr_train = fr[trial_ind, :][:, cell_ind];
fr_test = fr[test_ind, :][:, cell_ind];
fr_shuffle_train = fr_shuffle[trial_ind, :][:, cell_ind]
ot,st = cros_validate(number_lambd, X_train, Y_train, X_test, Y_test, X_shuffle_train, Y_shuffle_train)
fr_ot, fr_st = cros_validate(number_lambd, fr_train, Y_train, fr_test, Y_test, fr_shuffle_train, Y_shuffle_train)
ot_sum += ot.min()
st_sum += st.min()
fr_sum += fr_ot.min()
fr_st_sum += fr_st.min()
original_vector = original_vector + [1-(ot_sum/number_of_repeats)]
shuffle_vector = shuffle_vector+ [1-(st_sum/number_of_repeats)]
fr_vector = fr_vector+ [1-(fr_sum/number_of_repeats)]
fr_shuffle_vector = fr_shuffle_vector + [1-(fr_st_sum/number_of_repeats)]
t = range_of_num
plt.figure()
plt.plot(t, original_vector, t, shuffle_vector, 'r--', t, fr_vector, 'k', t, fr_shuffle_vector)
plt.legend(['Sim (Ca)', 'Shuf (Ca)', 'Sim (FR)', 'Shuf (FR)'])
plt.show()
def svm_decoder(X_original,Y_original,fr,dg):
X_shuffle = np.zeros(X_original.shape)
X_original.shape
fr_shuffle = np.zeros(X_original.shape)
Y_shuffle = Y_original.copy()
# Shuffle
for ci, on in enumerate(dg.stim_table.orientation.unique()):
for di, tf in enumerate(dg.stim_table['temporal_frequency'].unique()):
unique_index = dg.stim_table[
(dg.stim_table.orientation == on) & (dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(X_original[unique_index])
fr_shuffle[unique_index]= (fr[unique_index])[ixi,ixj]
number_trials = 100;
number_cells = 50;
number_test = 30;
number_lambd = 4;
number_of_repeats = 25;
shuffle_vector = []
original_vector = []
fr_shuffle_vector = []
fr_vector = []
for nr in range(number_of_repeats):
print(nr)
cell_ind = np.random.choice(X_original.shape[1], number_cells, replace=False);
trial_ind = np.random.choice(len(Y_original), number_trials, replace=False);
test_ind = np.random.choice(len(Y_original), number_test, replace=False);
X_train = X_original[trial_ind, :][:, cell_ind];
Y_train = Y_original[trial_ind];
X_test = X_original[test_ind, :][:, cell_ind];
Y_test = Y_original[test_ind];
X_shuffle_train = X_shuffle[trial_ind, :][:, cell_ind]
Y_shuffle_train = Y_shuffle[trial_ind]
fr_train = fr[trial_ind, :][:, cell_ind];
fr_test = fr[test_ind, :][:, cell_ind];
fr_shuffle_train = fr_shuffle[trial_ind, :][:, cell_ind]
ot,st = cros_validate(number_lambd, X_train, Y_train, X_test, Y_test, X_shuffle_train, Y_shuffle_train)
fr_ot, fr_st = cros_validate(number_lambd, fr_train, Y_train, fr_test, Y_test, fr_shuffle_train, Y_shuffle_train)
original_vector = original_vector + [1-ot.min()]
shuffle_vector = shuffle_vector+ [1-st.min()]
fr_vector = fr_vector+ [1-fr_ot.min()]
fr_shuffle_vector = fr_shuffle_vector + [1-fr_st.min()]
plt.figure()
plt.boxplot([original_vector, shuffle_vector, fr_vector, fr_shuffle_vector])
plt.xticks(np.arange(4) + 1, ('simultaneous (Ca)', 'shuffled (Ca)', 'simultaneous (f. rate)', 'shuffled_fr (f. rate)'))
plt.ylim((0, 1))
plt.ylabel('Decoder Performance')
plt.show()
def fisher_analysis(X,dg):
X_shuffle = np.zeros(X.shape)
# Shuffle
mu = [];
C = [];
C2 = [];
fisher_info = 0
fisher_info_shuffle = 0
for di, tf in enumerate([dg.stim_table['temporal_frequency'].unique()[0]]):
for ci, on in enumerate(dg.stim_table.orientation.unique()):
unique_index = dg.stim_table[
(dg.stim_table.orientation == on) & (dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(X[unique_index])
mu = mu + [(X[unique_index]).mean(axis=0)]
print(mu[0].shape)
temp_X = X[unique_index];
temp_X_shuffle = X_shuffle[unique_index];
C = C + [np.cov(temp_X.T)]
C2 = C2 + [np.cov(temp_X_shuffle.T)]
print(fisher_information(mu, C))
fisher_info +=fisher_information(mu, C)
fisher_info_shuffle +=fisher_information(mu, C2)
return fisher_info, fisher_info_shuffle
def svm_decoder_dsi(X_original, Y_original, fr, dg, dsi):
X_shuffle = np.zeros(X_original.shape)
fr_shuffle = np.zeros(X_original.shape)
Y_shuffle = Y_original.copy()
# Shuffle
for ci, on in enumerate(dg.stim_table.orientation.unique()):
for di, tf in enumerate(dg.stim_table['temporal_frequency'].unique()):
unique_index = dg.stim_table[(dg.stim_table.orientation == on) & (
dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(
X_original[unique_index])
fr_shuffle[unique_index] = (fr[unique_index])[ixi, ixj]
number_trials = 100
number_cells = 50
number_test = 30
number_lambd = 3
number_of_repeats = 30
range_of_num = range(5, 55, 5)
shuffle_vector = []
original_vector = []
fr_shuffle_vector = []
fr_vector = []
for bc, nb in enumerate(range_of_num):
ot_sum = 0
st_sum = 0
fr_sum = 0
fr_st_sum = 0
cell_ind = (np.argsort(dsi.flatten()))[-nb - 1:-1]
for nr in range(number_of_repeats):
print(nb, ':', nr)
trial_ind = np.random.choice(len(Y_original),
number_trials,
replace=False)
test_ind = np.random.choice(len(Y_original),
number_test,
replace=False)
X_train = X_original[trial_ind, :][:, cell_ind]
Y_train = Y_original[trial_ind]
print('should be 8: ', len(np.unique(Y_train)))
X_test = X_original[test_ind, :][:, cell_ind]
Y_test = Y_original[test_ind]
X_shuffle_train = X_shuffle[trial_ind, :][:, cell_ind]
Y_shuffle_train = Y_shuffle[trial_ind]
fr_train = fr[trial_ind, :][:, cell_ind]
fr_test = fr[test_ind, :][:, cell_ind]
fr_shuffle_train = fr_shuffle[trial_ind, :][:, cell_ind]
ot, st = cros_validate(number_lambd, X_train, Y_train, X_test,
Y_test, X_shuffle_train, Y_shuffle_train)
fr_ot, fr_st = cros_validate(number_lambd, fr_train, Y_train,
fr_test, Y_test, fr_shuffle_train,
Y_shuffle_train)
ot_sum += ot.min()
st_sum += st.min()
fr_sum += fr_ot.min()
fr_st_sum += fr_st.min()
original_vector = original_vector + [1 - (ot_sum / number_of_repeats)]
shuffle_vector = shuffle_vector + [1 - (st_sum / number_of_repeats)]
fr_vector = fr_vector + [1 - (fr_sum / number_of_repeats)]
fr_shuffle_vector = fr_shuffle_vector + [
1 - (fr_st_sum / number_of_repeats)
]
t = range_of_num
plt.figure()
plt.plot(t, original_vector, t, shuffle_vector, 'r--', t, fr_vector, 'k',
t, fr_shuffle_vector)
plt.legend(['Sim (Ca)', 'Shuf (Ca)', 'Sim (FR)', 'Shuf (FR)'])
plt.show()
def svm_decoder(X_original, Y_original, fr, dg):
X_shuffle = np.zeros(X_original.shape)
X_original.shape
fr_shuffle = np.zeros(X_original.shape)
Y_shuffle = Y_original.copy()
# Shuffle
for ci, on in enumerate(dg.stim_table.orientation.unique()):
for di, tf in enumerate(dg.stim_table['temporal_frequency'].unique()):
unique_index = dg.stim_table[(dg.stim_table.orientation == on) & (
dg.stim_table['temporal_frequency'] == tf)].index
X_shuffle[unique_index], ixi, ixj = permute_columns(
X_original[unique_index])
fr_shuffle[unique_index] = (fr[unique_index])[ixi, ixj]
number_trials = 100
number_cells = 50
number_test = 30
number_lambd = 4
number_of_repeats = 30
shuffle_vector = []
original_vector = []
fr_shuffle_vector = []
fr_vector = []
for nr in range(number_of_repeats):
print(nr)
cell_ind = np.random.choice(X_original.shape[1],
number_cells,
replace=False)
trial_ind = np.random.choice(len(Y_original),
number_trials,
replace=False)
test_ind = np.random.choice(len(Y_original),
number_test,
replace=False)
X_train = X_original[trial_ind, :][:, cell_ind]
Y_train = Y_original[trial_ind]
X_test = X_original[test_ind, :][:, cell_ind]
Y_test = Y_original[test_ind]
X_shuffle_train = X_shuffle[trial_ind, :][:, cell_ind]
Y_shuffle_train = Y_shuffle[trial_ind]
fr_train = fr[trial_ind, :][:, cell_ind]
fr_test = fr[test_ind, :][:, cell_ind]
fr_shuffle_train = fr_shuffle[trial_ind, :][:, cell_ind]
ot, st = cros_validate(number_lambd, X_train, Y_train, X_test, Y_test,
X_shuffle_train, Y_shuffle_train)
fr_ot, fr_st = cros_validate(number_lambd, fr_train, Y_train, fr_test,
Y_test, fr_shuffle_train, Y_shuffle_train)
original_vector = original_vector + [1 - ot.min()]
shuffle_vector = shuffle_vector + [1 - st.min()]
fr_vector = fr_vector + [1 - fr_ot.min()]
fr_shuffle_vector = fr_shuffle_vector + [1 - fr_st.min()]
plt.figure()
plt.boxplot(
[original_vector, shuffle_vector, fr_vector, fr_shuffle_vector])
plt.xticks(
np.arange(4) + 1, ('simultaneous (Ca)', 'shuffled (Ca)',
'simultaneous (f. rate)', 'shuffled_fr (f. rate)'))
plt.ylim((0, 1))
plt.ylabel('Test Error Rate')
plt.show()