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本文整理汇总了Python中math.log2函数的典型用法代码示例。如果您正苦于以下问题：Python log2函数的具体用法？Python log2怎么用？Python log2使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了log2函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: log_probability
def log_probability( self, sequence, transitions_weight = None, outputs_weight = 1 ):
"""
Returns the log-probability of the given symbol sequence. If the
sequence is labelled, then returns the joint log-probability of the
symbol, state sequence. Otherwise, uses the forward algorithm to find
the log-probability over all label sequences.
:return: the log-probability of the sequence
:rtype: float
:param sequence: the sequence of symbols which must contain the TEXT
property, and optionally the TAG property
:type sequence: Token
"""
if transitions_weight is None:
transitions_weight = 1
sequence = self._transform( sequence )
T = len( sequence )
EPSILON = ''
channelModel = 0
sourceModel = 0
if T > 0 and sequence[ 0 ][ _TAG ] is not None:
last_state = sequence[ 0 ][ _TAG ]
if last_state != EPSILON:
if transitions_weight:
sourceModel += transitions_weight * self._priors.logprob( last_state )
else:
if transitions_weight:
sourceModel += transitions_weight * math.log2( globalModelParameters.EpsilonTransition )
channelModel += outputs_weight * self._output_logprob( last_state, sequence[ 0 ][ _TEXT ] )
for t in range( 1, T ):
state = sequence[ t ][ _TAG ]
if last_state != EPSILON:
if state != EPSILON:
if transitions_weight:
sourceModel += transitions_weight * self._transitions[ last_state ].logprob( state )
else:
if transitions_weight:
sourceModel += transitions_weight * math.log2( globalModelParameters.EpsilonTransition )
else:
# check if last_state is epsilon; if so then transition with probability of Epsilon
if transitions_weight:
sourceModel += transitions_weight * math.log2( globalModelParameters.EpsilonTransition )
channelModel += outputs_weight * self._output_logprob( state, sequence[ t ][ _TEXT ] )
last_state = state
# FIXME changed exponentiation
return { 'HMMtotal': (sourceModel + channelModel),
'HMMchannel': channelModel,
'HMMsource': sourceModel,
'sequence': sequence}开发者ID:jcavalieri8619，项目名称:OCRerror_correct，代码行数:60，代码来源:HiddenMarkovModel.py
示例2: run_simulation
def run_simulation(num_blocks_per_set, num_words_per_block, cache_size,
replacement_policy, num_addr_bits, word_addrs):
num_blocks = cache_size // num_words_per_block
num_sets = num_blocks // num_blocks_per_set
# Ensure that the number of bits used to represent each address is always
# large enough to represent the largest address
num_addr_bits = max(num_addr_bits, int(math.log2(max(word_addrs))) + 1)
num_offset_bits = int(math.log2(num_words_per_block))
num_index_bits = int(math.log2(num_sets))
num_tag_bits = num_addr_bits - num_index_bits - num_offset_bits
refs = get_addr_refs(
word_addrs, num_addr_bits,
num_offset_bits, num_index_bits, num_tag_bits)
cache, ref_statuses = read_refs_into_cache(
num_sets, num_blocks_per_set, num_index_bits,
num_words_per_block, replacement_policy, refs)
print()
display_addr_refs(refs, ref_statuses)
print()
display_cache(cache)
print()开发者ID:chubbymaggie，项目名称:cache-simulator，代码行数:27，代码来源:simulator.py
示例3: calc_posprob
def calc_posprob(sentence,file1,file2):
prob_p =math.log2(pos_count_sep/(pos_count_sep+neg_count_sep))
voca=open(file1).read()
vocab=voca.split()
with open(file1) as f:
vocab_len= sum(1 for _ in f)
pos_word=open(file2).read()
pos_words=pos_word.split()
#with open(file1) as f:
# total_pos= sum(1 for _ in f)
total_pos=sum_words(file2)
for word in sentence:
if word in vocab:
if word in pos_words:
index= pos_words.index(word)
count= int(pos_words[index+1])
prob_1= math.log2(count+1/(total_pos+vocab_len))
prob_p= prob_p+prob_1
else:
prob_1 = math.log2(1/(total_pos+vocab_len))
prob_p= prob_p+prob_1
return prob_p开发者ID:sowmyabejjipuram，项目名称:NLP，代码行数:25，代码来源:naive_bayes_with_cross_validation.py
示例4: clog
def clog(pagerank):
vector = list(sorted(pagerank, reverse=True))
k = [math.log2(i) for i in range(1, len(vector) + 1)]
y = [math.log2(i) for i in vector]
A = np.vstack([k, np.ones(len(k))]).T
m, c = np.linalg.lstsq(A, y)[0]
return m开发者ID:KoIIdun，项目名称:PageRank-gradient，代码行数:7，代码来源:tests.py
示例5: test_tag2
def test_tag2(self):
tagset = {"D", "N", "V"}
trans = {
("", ""): {"D": 1.0},
("", "D"): {"N": 1.0},
("D", "N"): {"V": 0.8, "N": 0.2},
("N", "N"): {"V": 1.0},
("N", "V"): {"": 1.0},
}
out = {"D": {"the": 1.0}, "N": {"dog": 0.4, "barks": 0.6}, "V": {"dog": 0.1, "barks": 0.9}}
hmm = HMM(3, tagset, trans, out)
tagger = ViterbiTagger(hmm)
x = "the dog barks".split()
y = tagger.tag(x)
pi = {
0: {("", ""): (log2(1.0), [])},
1: {("", "D"): (log2(1.0), ["D"])},
2: {("D", "N"): (log2(0.4), ["D", "N"])},
3: {
("N", "V"): (log2(0.8 * 0.4 * 0.9), ["D", "N", "V"]),
("N", "N"): (log2(0.2 * 0.4 * 0.6), ["D", "N", "N"]),
},
}
self.assertEqualPi(tagger._pi, pi)
self.assertEqual(y, "D N V".split())开发者ID:PLN-FaMAF，项目名称:PLN-2015，代码行数:28，代码来源:test_viterbi_tagger.py
示例6: test_effective_window_size
def test_effective_window_size(self):
log_window_sizes = [math.log2(z) for z in self.window_sizes]
plot = PointPlot()
plot.new_plot("Effective Window Size", rows=1, num_curves=self.num_estimations+1)
avg_err_bayes = self.get_errors(self.num_estimations)
for i in range (0,len(self.window_sizes)):
for k in range (0, self.num_estimations+1):
self.print_values(k, self.window_sizes[i], avg_err_bayes[i][k], avg_err_bayes[i][0])
for k in range(0, len(avg_err_bayes[0])): # which is numestimations+1
k_array = avg_err_bayes[:,k]
log_k_array = [math.log2(y) for y in k_array]
if k == 0:
plot.add_data_to_plot(log_k_array,log_window_sizes,label = "Naive ("+str(k)+" Shifts)")
else:
plot.add_data_to_plot(log_k_array, log_window_sizes, label=str(k)+" Shifts")
"""naive = avg_err_bayes[0]
avg_err_naive = [naive]* len(avg_err_bayes)
plot.add_to_plot()"""
plot.create_legend()
plot.save_plot("effective_window_size_plot")开发者ID:alexlafleur，项目名称:LDStreamHMMLearn，代码行数:28，代码来源:effective_window_size.py
示例7: test_EquationBC_mixedpoisson_matrix_fieldsplit
def test_EquationBC_mixedpoisson_matrix_fieldsplit(eq_type, mat_type, porder):
# Mixed poisson with EquationBCs
# aij with fieldsplit pc
solver_parameters = {"mat_type": mat_type,
"ksp_type": "gmres",
"ksp_rtol": 1.e-10,
"ksp_atol": 1.e-10,
"ksp_max_it": 500000,
"pc_type": "fieldsplit",
"pc_fieldsplit_type": "schur",
"pc_fieldsplit_schur_fact_type": "full",
"fieldsplit_0_ksp_type": "gmres",
"fieldsplit_0_pc_type": "asm",
"fieldsplit_0_ksp_rtol": 1.e-12,
"fieldsplit_1_ksp_type": "gmres",
"fieldsplit_1_ksp_rtol": 1.e-12,
"fieldsplit_1_pc_type": "none"}
err = []
if eq_type == "linear":
for i, mesh_num in enumerate([8, 16]):
err.append(linear_poisson_mixed(solver_parameters, mesh_num, porder))
elif eq_type == "nonlinear":
for i, mesh_num in enumerate([8, 16]):
err.append(nonlinear_poisson_mixed(solver_parameters, mesh_num, porder))
assert(abs(math.log2(err[0][0]) - math.log2(err[1][0]) - (porder+1)) < 0.03)开发者ID:firedrakeproject，项目名称:firedrake，代码行数:29，代码来源:test_equation_bcs.py
示例8: __create_tournament_tree
def __create_tournament_tree(self):
'''
Creates list for every rounds. Connects every list item between other
items, that connections makes tournament tree.
@return: list of interconnected list items
'''
tournament_rounds = []
# create lists for every round
for i in range(int(math.log2(self.competitors_count))):
round_list = self._init_round_list(i)
tournament_rounds.append(round_list)
# make interconnections between rounds - tournament tree
for i in range(int(math.log2(self.competitors_count - 1))):
if len(tournament_rounds[- 1 - i]) > 1:
for j in range(len(tournament_rounds[- 1 - i]) // 2):
k = (2 * j)
tournament_rounds[- 1 - i][k].next_round = \
tournament_rounds[- 1 - i - 1][j]
tournament_rounds[- 1 - i][k + 1].next_round = \
tournament_rounds[- 1 - i - 1][j]
tournament_rounds[- 1 - i - 1][j].previous_match1 = \
tournament_rounds[- 1 - i][k]
tournament_rounds[- 1 - i - 1][j].previous_match2 = \
tournament_rounds[- 1 - i][k + 1]
# set current round variable to index for the first round
self.__current_round = len(tournament_rounds) - 1
# return all rounds
return tournament_rounds开发者ID:adpro，项目名称:TournamentsTest，代码行数:29，代码来源:tournaments.py
示例9: qsort_based_counter
def qsort_based_counter(a, b, x):
len_x = len(x)
result = [0] * len_x
checked = {}
len_a = len(a)
len_b = len(b)
if not len_a:
return result
qsort(a, 0, len_a, math.floor(math.log2(len_a)))
qsort(b, 0, len_b, math.floor(math.log2(len_b)))
print(a)
print(b)
# a = sorted(a)
# b = sorted(b)
for i in range(0, len(x)):
if x[i] < a[0]:
continue
if x[i] in checked:
result[i] = result[checked[x[i]]]
else:
a_idx = bisect.bisect_right(a, x[i])
b_idx = bisect.bisect_left(b, x[i])
result[i] = a_idx - b_idx
checked[x[i]] = i
return result开发者ID:Se7ge，项目名称:csc，代码行数:25，代码来源:4413_2.py
示例10: GetSampleLincData
def GetSampleLincData(sample, linc_exp):
"""
Get the data for each linc for that sample. Get log2 fold change for FPKM compared to average and median of all samples for the linc.
Args:
sample = Sample from the input file name.
linc_exp = Name of file containing the expression data for each linc in every sample.
Returns:
linc_dict = Dict containing signal of every SE position for the sample {(chr, (start, stop)): ((linc_id, linc_name), signal)}
"""
# Dict to hold data.
linc_dict = {}
with open(linc_exp) as f:
# Get the sample index.
header = f.readline().strip()
sample_idx = GetSampleIdx(header, sample)
for line in f:
line = line.strip().split("\t")
data = [float(x) for x in line[5:]] # Convert all data to floats.
linc_med = log2(float(median(data))) # Get log2 median of list.
linc_avg = log2(float(mean(data))) # Get log2 average of the list.
linc_val = log2(float(line[sample_idx])) # Get log2 of the linc FPKM for the sample.
linc_med_FC = linc_val - linc_med
linc_avg_FC = linc_val - linc_avg
# Grab data and add to the dict.
chrom, start, stop, linc_id, linc_name = line[0], int(line[1]), int(line[2]), line[3], line[4]
linc_dict[(chrom, (start, stop))] = ((linc_id, linc_name), (linc_med_FC, linc_avg_FC))
return linc_dict开发者ID:j-andrews7，项目名称:Pipelines，代码行数:35，代码来源:get_sample_linc_cnv_loads.py
示例11: I
def I(self, term, cluster):
n = len(self.docVector)
n00 = n10 = n11 = n01 = 0
for id in self.docVector:
if self.docCluster[id] == cluster:
if term in self.docVector[id].dict.keys():
n11 += 1
else:
n01 += 1
else:
if term in self.docVector[id].dict.keys():
n10 += 1
else:
n00 += 1
n1_ = n10 + n11
n_1 = n01 + n11
n0_ = n00 + n01
n_0 = n00 + n10
# #print('cluster : '+cluster.__str__())
# #print('n00 = ',n00)
# #print('n01 = ', n01)
# #print('n10 = ',n10)
# #print('n11 = ', n11)
a1 = n11 / n * log2(n * n11 / (n1_ * n_1)) if n11 != 0 else 0
a2 = n01 / n * log2(n * n01 / (n0_ * n_1)) if n01 != 0 else 0
a3 = n10 / n * log2(n * n10 / (n1_ * n_0)) if n10 != 0 else 0
a4 = n00 / n * log2(n * n00 / (n0_ * n_0)) if n00 != 0 else 0
return a1 +a2 + a3 + a4开发者ID:MJafarMashhadi，项目名称:MiniGoogle，代码行数:29，代码来源:K_means.py
示例12: channelModel
def channelModel( candidate_object ):
partitionProbData = [ ]
for partition in candidate_object[ 'partitions' ]:
partitionProbData.append( HMMmodel.log_probability( partition,
transitions_weight = globalModelParameters.TransitionWeight ) )
partitionProbData.sort(key=lambda arg: arg['HMMtotal'],reverse=True)
TopPartitionsProbData=partitionProbData[:globalModelParameters.NUM_PARTITIONS]
candidate_object.pop( 'partitions' )
candidate_object[ 'totalProb' ] = 0
candidate_object[ 'channelProb' ] = round( math.log2(sum(map(lambda arg: 2**arg[ 'HMMtotal' ],TopPartitionsProbData),0)) ,
3 )
candidate_object[ 'langProb' ] = 0
candidate_object[ 'HMMchannel' ] = round( math.log2(sum(map(lambda arg: 2**arg[ 'HMMchannel' ],TopPartitionsProbData),0)) ,
3 )
candidate_object[ 'HMMsource' ] = round( math.log2(sum(map(lambda arg: 2**arg[ 'HMMsource' ],TopPartitionsProbData),0)) , 3 )
candidate_object[ 'maxPartition' ] = TopPartitionsProbData[0]['sequence']
candidate_object['topPartitionsDict'] = TopPartitionsProbData
return candidate_object开发者ID:jcavalieri8619，项目名称:OCRerror_correct，代码行数:34，代码来源:ErrorCorrector.py
示例13: score
def score(self, text):
total_score = 0
prev_word = None
for current_word in text:
current_score = 0
#print('current word is {}'.format(current_word))
if current_word in self.pos_features:
current_score += math.log2(self.pos_features[current_word])
# #print('+1')
if current_word in self.neg_features:
current_score -= math.log2(self.neg_features[current_word])
# print('-1')
if prev_word is not None:
# print('prev word is {}'.format(prev_word))
if prev_word in self.inc_features:
current_score *= 1.5
# print('*2')
elif prev_word in self.dec_features:
current_score /= 1.5
# print('/2')
elif prev_word in self.inv_features:
current_score *= -1.0
# print('-')
prev_word = current_word
total_score += current_score
return total_score开发者ID:Finaleblue，项目名称:Cooper_ece467，代码行数:29，代码来源:tag_based_analysis.py
示例14: NaiveBayes
def NaiveBayes(class_list,variables_counter,path):
test = pd.read_csv(path,sep='\t',names=['num','class','desc'],header = None)
final_list = []
desc_list = test['desc'].tolist()
test_class = test['class']
total_train_files = sum(class_count.values())
i = 0
for line in desc_list:
class_prob.clear()
if type(line) is not str:
continue
record = line.split()
for word in record:
for classes in class_list:
prob_word_in_class = 0.0
class_counter = complete_dict[classes]
class_desc_overall_count = sum(class_counter.values())
class_word_unique_count = len(variables_counter)
word_count = class_counter.get(word,0)
prob_word_in_class = ( ((math.log2((word_count+1)) - math.log2((class_desc_overall_count + class_word_unique_count)))))
if class_prob.get(classes,0) != 0:
class_prob[classes] = class_prob[classes] + prob_word_in_class
else:
class_prob[classes] = prob_word_in_class
class_prob[classes] = class_prob[classes] + math.log2(class_count[classes]/total_train_files)
#print(class_prob,max(class_prob,key=class_prob.get))
final_list.append(max(class_prob,key=class_prob.get))
accuracy(final_list,test) 开发者ID:yagamiram，项目名称:Multinomial_Classifier_for_Text_Classification，代码行数:29，代码来源:NaiveBayes.py
示例15: computeHash
def computeHash(inputFile):
# Initialize a list for storing each transaction from the file
try:
transactionsList = open(inputFile, 'rt').read().split('\n')
except FileNotFoundError:
print("The file cannot be found. Please enter a valid name.")
return
# If there's a newline character at the end, account for it
if len(transactionsList[len(transactionsList) - 1]) == 0:
transactionsList = transactionsList[:len(transactionsList) - 1]
nextLogOfTwo = math.log2(len(transactionsList))
# If the number of transactions in the list is not a power of 2, then append the string 'null' into it until it is
if not nextLogOfTwo.is_integer():
# Find what the next log of two is
nextLogOfTwo = math.ceil(math.log2(len(transactionsList)))
targetNumOfList = int(math.pow(2, nextLogOfTwo))
# And append 'null'
for i in range(0, targetNumOfList - len(transactionsList), 1):
transactionsList.append('null')
else:
nextLogOfTwo = int(nextLogOfTwo)
# Encode each of the items in transactionsList to their corresponding representations in bytes
for indexOfTrans in range(0, len(transactionsList), 1):
transactionsList[indexOfTrans] = bytes(transactionsList[indexOfTrans], 'utf-8')
hashes = []
currLevelHash = list(transactionsList)
nextLevelHash = []
for j in range(0, len(currLevelHash), 1):
hashOfEachElem = hashlib.sha256()
hashOfEachElem.update(currLevelHash[j])
nextLevelHash.append(hashOfEachElem)
currLevelHash = nextLevelHash
# Now start hashing and concatenating each pair of elements up till nextLogOfTwo
for i in range(0, nextLogOfTwo, 1):
nextLevelHash = []
for j in range(0, len(currLevelHash) - 1, 2):
hashOfFirstElem = currLevelHash[j].hexdigest()
hashOfSecondElem = currLevelHash[j+1].hexdigest()
bothElemsConcatenated = hashOfFirstElem + hashOfSecondElem
hashOfBothElems = hashlib.sha256()
hashOfBothElems.update(bytes(bothElemsConcatenated, 'utf-8'))
nextLevelHash.append(hashOfBothElems)
currLevelHash = nextLevelHash
# Set hashes to be equal to currLevelHash
hashes = currLevelHash
# And return the hexdigest of the root hash
return hashes[0].hexdigest()开发者ID:RylanSchaeffer，项目名称:ECS198-Cryptocurrency-Technologies，代码行数:60，代码来源:KoradiaSohamHW2Submission.py
注：本文中的math.log2函数示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。
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