def membership(self):
x = self.__training_set
c = self.__centers
M, _ = x.shape
C, _ = c.shape
r = zeros((M, C))
m1 = 1. / (self.fuzzyness_coefficient - 1.)
for k in range(M):
den = numpy_sum((x[k] - c)**2., axis=1)
frac = outer(den, 1. / den)**m1
r[k, :] = 1. / numpy_sum(frac, axis=1)
self.__membership_degrees = r
return self.__membership_degrees
def compute_mass_grid(
valuefield,
areafield,
dofrac=False,
fracfield=None,
uninitval=422397696.0,
):
"""Compute the mass of a data field.
:Parameters:
valuefield: ESMF.Field
This contains data values of a field built on the cells of
a grid.
areafield: ESMF.Field
This contains the areas associated with the grid cells.
fracfield: ESMF.Field
This contains the fractions of each cell which contributed
to a regridding operation involving 'valuefield.
dofrac: `bool`
This gives the option to not use the 'fracfield'.
uninitval: `float`
The value uninitialised cells take.
:Returns:
`float`
The mass of the data field is computed.
"""
mass = 0.0
areafield.get_area()
ind = numpy_where(valuefield.data != uninitval)
if dofrac:
mass = numpy_sum(
areafield.data[ind]
* valuefield.data[ind]
* fracfield.data[ind]
)
else:
mass = numpy_sum(areafield.data[ind] * valuefield.data[ind])
return mass
def sample_size_f(a, axis=None, masked=False):
"""Return the sample size.
:Parameters:
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
:Returns:
`numpy.ndarray`
"""
if masked:
N = numpy_sum(~a.mask, axis=axis, dtype=float)
if not numpy_ndim(N):
N = numpy_asanyarray(N)
else:
if axis is None:
N = numpy_array(a.size, dtype=float)
else:
shape = a.shape
N = numpy_empty(shape[:axis] + shape[axis + 1 :], dtype=float)
N[...] = shape[axis]
# --- End: if
return asanyarray(N)
def summarize_pcoas(master_pcoa, support_pcoas, method='IQR', apply_procrustes=True):
"""returns the average PCoA vector values for the support pcoas
Also returns the ranges as calculated with the specified method.
The choices are:
IQR: the Interquartile Range
ideal fourths: Ideal fourths method as implemented in scipy
"""
if apply_procrustes:
# perform procrustes before averaging
support_pcoas = [list(sp) for sp in support_pcoas]
master_pcoa = list(master_pcoa)
for i, pcoa in enumerate(support_pcoas):
master_std, pcoa_std, m_squared = procrustes(master_pcoa[1],pcoa[1])
support_pcoas[i][1] = pcoa_std
master_pcoa[1] = master_std
m_matrix = master_pcoa[1]
m_eigvals = master_pcoa[2]
m_names = master_pcoa[0]
jn_flipped_matrices = []
all_eigvals = []
for rep in support_pcoas:
matrix = rep[1]
eigvals = rep[2]
all_eigvals.append(eigvals)
jn_flipped_matrices.append(_flip_vectors(matrix, m_matrix))
matrix_average, matrix_low, matrix_high = _compute_jn_pcoa_avg_ranges(\
jn_flipped_matrices, method)
#compute average eigvals
all_eigvals_stack = vstack(all_eigvals)
eigval_sum = numpy_sum(all_eigvals_stack, axis=0)
eigval_average = eigval_sum / float(len(all_eigvals))
return matrix_average, matrix_low, matrix_high, eigval_average, m_names
def summarize_pcoas(master_pcoa, support_pcoas, method='IQR', apply_procrustes=True):
"""returns the average PCoA vector values for the support pcoas
Also returns the ranges as calculated with the specified method.
The choices are:
IQR: the Interquartile Range
ideal fourths: Ideal fourths method as implemented in scipy
"""
if apply_procrustes:
# perform procrustes before averaging
support_pcoas = [list(sp) for sp in support_pcoas]
master_pcoa = list(master_pcoa)
for i, pcoa in enumerate(support_pcoas):
master_std, pcoa_std, m_squared = procrustes(master_pcoa[1],pcoa[1])
support_pcoas[i][1] = pcoa_std
master_pcoa[1] = master_std
m_matrix = master_pcoa[1]
m_eigvals = master_pcoa[2]
m_names = master_pcoa[0]
jn_flipped_matrices = []
all_eigvals = []
for rep in support_pcoas:
matrix = rep[1]
eigvals = rep[2]
all_eigvals.append(eigvals)
jn_flipped_matrices.append(_flip_vectors(matrix, m_matrix))
matrix_average, matrix_low, matrix_high = _compute_jn_pcoa_avg_ranges(\
jn_flipped_matrices, method)
#compute average eigvals
all_eigvals_stack = vstack(all_eigvals)
eigval_sum = numpy_sum(all_eigvals_stack, axis=0)
eigval_average = eigval_sum / float(len(all_eigvals))
return matrix_average, matrix_low, matrix_high, eigval_average, m_names
def downsample_presence_array(presence_array, resolution):
"""
Downsamples a 1D presence array given the specified resolution.
Sums the presence across bins given the resolution size, so
[0, 1, 1, 1, 0, 0] for resolution == 3 becomes
[2, 1]
"""
return [numpy_sum(presence_array[index:index+resolution])
for index in range(0, len(presence_array), resolution)]
def fill_virtual_evidence_cells(prior_input_array, num_labels):
"""
For genomic positions which have at least one, but not all priors specified,
this function will apply a uniform prior to all remaining labels.
Indexes where no prior was ever specified will remain zero for downsampling
Example:
INPUT:
[[0.5, 0.2, None, None, None],
[None, 0.4, None, None, None],
[None, None, None, None, None]]
OUTPUT:
[[0.5, 0.2, 0.10, 0.10, 0.10],
[0.15, 0.4, 0.15, 0.15, 0.15],
[0.0 ... 0.0]]
"""
prior_array = zeros((len(prior_input_array), num_labels))
for index, prior_input in enumerate(prior_input_array):
# Only priors which were specified in the input file will be set
# Unset labels will still be none
num_prior_labels = numpy_sum(prior_input != None)
if num_prior_labels:
prior_list_values = list(filter(None, prior_input))
# Check if priors should be treated as ratios or percentages
if numpy_sum(prior_list_values) < 1:
remaining_probability = 1 - numpy_sum(prior_list_values)
else:
remaining_probability = 0
# divide remaining probability uniformly amongst the remaining labels
prior_input[prior_input == None] = (
remaining_probability / (num_labels - num_prior_labels))
prior_array[index] = prior_input
return prior_array
def normalise(raw_counts):
"""Normalise raw counts into relative abundance.
Args:
raw_counts (class `numpy.ndarray`): Array with raw count.
Returns:
class `numpy.ndarray`: Normalised data.
"""
sum_values = numpy_sum(raw_counts)
if not sum_values:
raise RuntimeWarning('All values in input are 0.')
return raw_counts / sum_values
def aggregate_level(results, position):
"""Aggregate abundance of metagenomes by taxonomic level.
Args:
results (dict): Path to results.
position (int): Position of level in the results.
Returns:
dict: Aggregated result for targeted taxonomy level.
"""
level_results = defaultdict(list)
for all_taxa in results:
taxa = all_taxa.split("\t")[position]
abundance = results[all_taxa]
level_results[taxa].append(abundance)
return {temp_taxa: numpy_sum(level_results[temp_taxa], axis=0) for temp_taxa in level_results}
def _compute_jn_pcoa_avg_ranges(jn_flipped_matrices, method):
"""Computes PCoA average and ranges for jackknife plotting
returns 1) an array of jn_averages
2) an array of upper values of the ranges
3) an array of lower values for the ranges
method: the method by which to calculate the range
IQR: Interquartile Range
ideal fourths: Ideal fourths method as implemented in scipy
"""
x,y = shape(jn_flipped_matrices[0])
all_flat_matrices = [matrix.ravel() for matrix in jn_flipped_matrices]
summary_matrix = vstack(all_flat_matrices)
matrix_sum = numpy_sum(summary_matrix, axis=0)
matrix_average = matrix_sum / float(len(jn_flipped_matrices))
matrix_average = matrix_average.reshape(x,y)
if method == 'IQR':
result = matrix_IQR(summary_matrix)
matrix_low = result[0].reshape(x,y)
matrix_high = result[1].reshape(x,y)
elif method == 'ideal_fourths':
result = idealfourths(summary_matrix, axis=0)
matrix_low = result[0].reshape(x,y)
matrix_high = result[1].reshape(x,y)
elif method == "sdev":
# calculate std error for each sample in each dimension
sdevs = zeros(shape=[x,y])
for j in xrange(y):
for i in xrange(x):
vals = array([pcoa[i][j] for pcoa in jn_flipped_matrices])
sdevs[i,j] = vals.std(ddof=1)
matrix_low = -sdevs/2
matrix_high = sdevs/2
return matrix_average, matrix_low, matrix_high
def _compute_jn_pcoa_avg_ranges(jn_flipped_matrices, method):
"""Computes PCoA average and ranges for jackknife plotting
returns 1) an array of jn_averages
2) an array of upper values of the ranges
3) an array of lower values for the ranges
method: the method by which to calculate the range
IQR: Interquartile Range
ideal fourths: Ideal fourths method as implemented in scipy
"""
x, y = shape(jn_flipped_matrices[0])
all_flat_matrices = [matrix.ravel() for matrix in jn_flipped_matrices]
summary_matrix = vstack(all_flat_matrices)
matrix_sum = numpy_sum(summary_matrix, axis=0)
matrix_average = matrix_sum / float(len(jn_flipped_matrices))
matrix_average = matrix_average.reshape(x, y)
if method == 'IQR':
result = matrix_IQR(summary_matrix)
matrix_low = result[0].reshape(x, y)
matrix_high = result[1].reshape(x, y)
elif method == 'ideal_fourths':
result = idealfourths(summary_matrix, axis=0)
matrix_low = result[0].reshape(x, y)
matrix_high = result[1].reshape(x, y)
elif method == "sdev":
# calculate std error for each sample in each dimension
sdevs = zeros(shape=[x, y])
for j in xrange(y):
for i in xrange(x):
vals = array([pcoa[i][j] for pcoa in jn_flipped_matrices])
sdevs[i, j] = vals.std(ddof=1)
matrix_low = -sdevs / 2
matrix_high = sdevs / 2
return matrix_average, matrix_low, matrix_high
def sample_size_f(a, axis=None, masked=False):
'''TODO
:Parameters:
axis: `int`, optional
non-negative
'''
if masked:
N = numpy_sum(~a.mask, axis=axis, dtype=float)
if not numpy_ndim(N):
N = numpy_asanyarray(N)
else:
if axis is None:
N = numpy_array(a.size, dtype=float)
else:
shape = a.shape
N = numpy_empty(shape[:axis] + shape[axis + 1:], dtype=float)
N[...] = shape[axis]
# --- End: if
return asanyarray(N)
def sum_squared(predictions, output_data):
return (1 / 2) * numpy_sum((output_data - predictions) ** 2)
def calc_stats(des_colors,curr_colors,curr_entered,req,X,n_to_try):
# Create modified desired/current list, so its in format [1,1,2,2,3,3]
desired = []
for i in range(0,3):
for j in range(0,des_colors[i]):
desired.append(i+1)
current = []
for i in range(0,3):
for j in range(0,curr_colors[i]):
current.append(i+1)
# Color weights
p = [r+X for r in req]
sump = float(numpy_sum(p))
Pr = p[0] / sump
Pg = p[1] / sump
Pb = p[2] / sump
N = sum(des_colors)
Nfact = factorial(N)
combs_iter = combinations_with_replacement([r+1 for r in range(3)],N)
# Bring this into a usable form
combs = []
for i in combs_iter:
combs.append(i)
Ncombs = len(combs)
Pcomb = []
Pperm = []
# Calculate probability of each combination
for i in combs:
Nr = i.count(1)
Ng = i.count(2)
Nb = i.count(3)
Pperm.append(Pr**Nr * Pg**Ng * Pb**Nb)
Pcomb.append(Nfact/(factorial(Nr)*factorial(Ng)*factorial(Nb)) * Pr**Nr * Pg**Ng * Pb**Nb)
# Create Transition Matrix
T = zeros((Ncombs,Ncombs))
count = 0
ind = 100 # Set high so that if we find current first, it'll be lower than ind
for i in combs:
T[count,:] = Pcomb[:]
# Self transition prob is lowered, can't return same permutation
T[count,count] = (Pcomb[count]-Pperm[count])/(1-Pperm[count])
total = numpy_sum(T[count,:])
T[count,:] = divide(T[count,:],total)
# If we reach the desired state we're done
if list(i) == desired:
T[count,:] = 0
T[count,count] = 1
ind = count
if list(i) == current:
curr_ind = count
if curr_ind > ind:
curr_ind-=1 # This adjusts the index to fit smaller matrix t
count += 1
# Create Fundamental Matrix & Identity Matrix
# Notation from http://en.wikipedia.org/wiki/Absorbing_Markov_chain
Q = zeros((Ncombs-1,Ncombs-1))
I = zeros((Ncombs-1,Ncombs-1))
for i in range(len(T)):
if i < ind:
xind = i
elif i > ind:
xind = i-1
else:
xind = -1
for j in range(len(T)):
if j < ind:
yind = j
elif j > ind:
yind = j-1
else:
yind = -1
if xind > -1 and yind > -1:
Q[xind,yind] = T[i,j]
if xind == yind:
I[xind,yind] = 1
Nmat = matrix(I-Q)
Nmat = Nmat.I
t = Nmat * ones((len(Nmat),1))
if curr_entered == 0:
mean_chromes = float(mean(t))
else:
mean_chromes = float(t[curr_ind])
# Modify T so that T[:,ind] represents the matrix R, probability of going from any transient state to the absorbing state, multiply by the probability of being in any transient state
T[ind,ind] = 0
R = zeros((Ncombs-1,1))
count = 0
for val in T[:,ind]:
if val != 0:
R[count,0] = val
count += 1
prob_trans = zeros((1,Ncombs-1))
count=0
for i in range(len(Pcomb)):
if i != ind:
prob_trans[0,count] = Pcomb[i]
count += 1
prob_trans = divide(prob_trans,numpy_sum(prob_trans))
prob_per_chrome = float(dot(prob_trans,R))
# Calculate Median & cdf after n_to_try chromes
stopping_point = 5000 # This is where we stop calculating exactly and start approximating
cum_prob_failure = 1
found_median = 0
# Calculate probability after some number of chromes (inexact)
curr_state = zeros((1,Ncombs-1))
if curr_entered == 0:
count = 0
for i in range(len(Pcomb)):
if i != ind:
curr_state[0,count] = Pcomb[i]
count += 1
else:
# We know where we're starting, so let's calculate more exactly
curr_state[0,curr_ind] = 1 # We know 100% what state we start in
# Force sum(curr_state) = 1
curr_state = divide(curr_state,numpy_sum(curr_state))
for i in range(stopping_point):
prob_failure = 1 - float(dot(curr_state,R))
cum_prob_failure = cum_prob_failure * prob_failure
curr_state = dot(curr_state,Q) # Update current state
curr_state = divide(curr_state,numpy_sum(curr_state)) # Normalize to create pdf
if i+1 == n_to_try:
prob_so_far = 1 - cum_prob_failure
prob_success = 1 - cum_prob_failure
if prob_success >= .5 and found_median==0:
median_chromes = i+1
found_median = 1
prob_per_chrome = float(dot(curr_state,R))
if n_to_try > stopping_point: # Approximate the remaining chromes using prob_per_chrome
n_leftover = n_to_try - stopping_point
cum_prob_failure = cum_prob_failure * (1-prob_per_chrome)**n_leftover
prob_so_far = 1 - cum_prob_failure
# We may need to still calculate the median
if found_median == 0:
# Calculate the median
# The solution for n_left for .5 = 1 - cum_prob_failure*(1-p)**n_left is
# (log(1/(2*cum_prob_failure)) + 2i * pi * n_inf)/log(1-p), where log is the natural log
# Taking the real portion of this complex number gives the median
n_inf = 1 # This can be any real integer
complex_num = (log(1/(2*(1-prob_so_far))) + 2*sqrt(-1)*pi*n_inf)/log(1-prob_per_chrome)
chromes_left = float(complex_num.real)
median_chromes = n_to_try + chromes_left
return median_chromes, mean_chromes, n_to_try, prob_so_far, prob_per_chrome
def process_data(request):
# Let's process all the input
error_list = []
[des_r,des_g,des_b], error_list = check_input(['des_r','des_g','des_b'],1,6,request,error_list)
[curr_r,curr_g,curr_b], error_list = check_input(['curr_r','curr_g','curr_b'],0,6,request,error_list)
[STR], error_list = check_input(['str'],0,'inf',request,error_list)
[DEX], error_list = check_input(['dex'],0,'inf',request,error_list)
[INT], error_list = check_input(['int'],0,'inf',request,error_list)
[X], error_list = check_input(['X'],1,'inf',request,error_list)
[n_to_try], error_list = check_input(['n_to_try'],1,'inf',request,error_list)
# Formatting
des_colors = [des_r,des_g,des_b]
curr_colors = [curr_r,curr_g,curr_b]
try:
if numpy_sum(curr_colors) > 0: # Did they enter the current colors?
curr_entered = 1
else:
curr_entered = 0
type_error = 0
except TypeError:
curr_entered = 0
type_error = 1
try:
numpy_sum(des_colors) > 0
except TypeError:
type_error = 1
# Default to this for error messages
c = { 'des_r': des_r,
'des_g': des_g,
'des_b': des_b,
'curr_r': curr_r,
'curr_g': curr_g,
'curr_b': curr_b,
'str': STR,
'dex': DEX,
'int': INT,
'X': X,
'median_chromes': 0,
'mean_chromes': 0,
'n_to_try': n_to_try,
'prob_so_far': str(1.0)}
# Check for Errors
if type_error == 0:
if numpy_sum(des_colors) == 0: # No desired colors entered
error_list.append("Please enter a valid desired item configuration")
elif des_colors == curr_colors: # Equivalent items entered
error_list.append("You apparently already have the item you want")
elif numpy_sum(des_colors) != numpy_sum(curr_colors) and curr_entered==1: # Diff num sockets
error_list.append("Your current item has a different number of sockets than your desired item")
# Quit on error
if len(error_list) > 0:
c['error_message'] = error_list
return c
req = [STR,DEX,INT]
# Perform statistics calculations
median_chromes, mean_chromes, n_to_try, prob_so_far, prob_per_chrome = calc_stats(des_colors,curr_colors,curr_entered,req,X,n_to_try)
# Calculate Vorici Results
vorici_1 = 4
vorici_2_same = 25
vorici_3_same = 285
vorici_2_diff = 15
vorici_3_diff = 100
# Determine possibly relevant Vorici mods
poss_vorici = []
if des_r >= 1:
poss_vorici.append([1,0,0])
if des_r >= 2:
poss_vorici.append([2,0,0])
if des_g >= 2:
poss_vorici.append([1,2,0])
if des_b >= 2:
poss_vorici.append([1,0,2])
if des_g >= 1:
poss_vorici.append([0,1,0])
if des_r >= 2:
poss_vorici.append([2,1,0])
if des_g >= 2:
poss_vorici.append([0,2,0])
if des_b >= 2:
poss_vorici.append([0,1,2])
if des_b >= 1:
poss_vorici.append([0,0,1])
if des_r >= 2:
poss_vorici.append([2,0,1])
if des_g >= 2:
poss_vorici.append([0,2,1])
if des_b >= 2:
poss_vorici.append([0,0,2])
# Calculate chances for each Vorici mod
# NOTE: Ignores curr_colors, also only capturing mean values
vorici_means = []
vorici_dict = {}
best_vorici = -1
best_vorici_mean = -1
for vorici_colors in poss_vorici:
altered_colors = subtract(des_colors,vorici_colors)
vor_median_chromes, vor_mean_chromes, vor_n_to_try, vor_prob_so_far, vor_prob_per_chrome = calc_stats(altered_colors,[0,0,0],0,req,X,n_to_try)
vorici_means.append(vor_mean_chromes)
if vor_mean_chromes < best_vorici_mean or best_vorici_mean == -1:
best_vorici_mean = vor_mean_chromes
best_vorici = vorici_colors
# Determine proper multiplier
if numpy_sum(best_vorici) == 1:
vorici_mult = vorici_1
elif numpy_sum(best_vorici) == 2:
if 1 in best_vorici:
vorici_mult = vorici_2_diff
else:
vorici_mult = vorici_2_same
elif numpy_sum(best_vorici) == 3:
if 3 in best_vorici:
vorici_mult = vorici_3_same
else:
vorici_mult = vorici_3_diff
best_vorici_cost = best_vorici_mean * vorici_mult
# Return Information
c = { 'des_r': des_r,
'des_g': des_g,
'des_b': des_b,
'curr_r': curr_r,
'curr_g': curr_g,
'curr_b': curr_b,
'str': STR,
'dex': DEX,
'int': INT,
'X': X,
'median_chromes': round(median_chromes,1),
'mean_chromes': round(mean_chromes,1),
'n_to_try': n_to_try,
'prob_so_far': str(round(prob_so_far*100,1)) + '%',
'n_prob': str(n_to_try) + '_' + str(prob_per_chrome),
'graph_url': 'graphs/' + str(n_to_try) + '_' + str(prob_per_chrome),
'intro_message': 0,
'vorici_colors': best_vorici,
'vorici_mean': best_vorici_mean,
'vorici_cost': best_vorici_cost}
return c
def cross_entropy(predictions, output_data):
return -numpy_sum(output_data * log(predictions))
def compute_centers(self):
mm = self.__membership_degrees**self.fuzzyness_coefficient
c = dot(self.__training_set.transpose(), mm) / numpy_sum(mm, axis=0)
self.__centers = c.transpose()
return self.__centers
def cross_entropy(predictions, output_data):
return -numpy_sum(output_data * log(predictions))
def sum_squared(predictions, output_data):
return (1 / 2) * numpy_sum((output_data - predictions)**2)
