BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def half_mag_amplitude_ratio(self, mag, avg, weight): """ Return ratio of amplitude of higher and lower magnitudes. A ratio of amplitude of higher and lower magnitudes than average, considering weights. This ratio, by definition, should be higher for EB than for others. ...
def half_mag_amplitude_ratio2(self, mag, avg): """ Return ratio of amplitude of higher and lower magnitudes. A ratio of amplitude of higher and lower magnitudes than average, considering weights. This ratio, by definition, should be higher for EB than for others. Param...
def get_eta(self, mag, std): """ Return Eta feature. Parameters ---------- mag : array_like An array of magnitudes. std : array_like A standard deviation of magnitudes. Returns ------- eta : float The value of ...
def slope_percentile(self, date, mag): """ Return 10% and 90% percentile of slope. Parameters ---------- date : array_like An array of phase-folded date. Sorted. mag : array_like An array of phase-folded magnitudes. Sorted by date. Return...
def get_cusum(self, mag): """ Return max - min of cumulative sum. Parameters ---------- mag : array_like An array of magnitudes. Returns ------- mm_cusum : float Max - min of cumulative sum. """ c = np.cumsum(mag ...
def get_features2(self): """ Return all features with its names. Returns ------- names : list Feature names. values : list Feature values """ feature_names = [] feature_values = [] # Get all the names of features....
def get_features_all(self): """ Return all features with its names. Regardless of being used for train and prediction. Sorted by the names. Returns ------- all_features : OrderedDict Features dictionary. """ features = {} # Get all ...
def init(device_id=None, random_seed=None): """Initialize Hebel. This function creates a CUDA context, CUBLAS context and initializes and seeds the pseudo-random number generator. Parameters: device_id : integer, optional The ID of the GPU device to use. If this is omitted, PyCUDA...
def inflate_context_tuple(ast_rootpath, root_env): """Instantiate a Tuple from a TupleNode. Walking the AST tree upwards, evaluate from the root down again. """ with util.LogTime('inflate_context_tuple'): # We only need to look at tuple members going down. inflated = ast_rootpath[0].eval(root_env) ...
def enumerate_scope(ast_rootpath, root_env=None, include_default_builtins=False): """Return a dict of { name => Completions } for the given tuple node. Enumerates all keys that are in scope in a given tuple. The node part of the tuple may be None, in case the binding is a built-in. """ with util.LogTime('enu...
def find_deref_completions(ast_rootpath, root_env=gcl.default_env): """Returns a dict of { name => Completions }.""" with util.LogTime('find_deref_completions'): tup = inflate_context_tuple(ast_rootpath, root_env) path = path_until(ast_rootpath, is_deref_node) if not path: return {} deref = pa...
def is_identifier_position(rootpath): """Return whether the cursor is in identifier-position in a member declaration.""" if len(rootpath) >= 2 and is_tuple_member_node(rootpath[-2]) and is_identifier(rootpath[-1]): return True if len(rootpath) >= 1 and is_tuple_node(rootpath[-1]): # No deeper node than tu...
def find_completions_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env): """Find completions at the cursor. Return a dict of { name => Completion } objects. """ q = gcl.SourceQuery(filename, line, col - 1) rootpath = ast_tree.find_tokens(q) if is_identifier_position(rootpath): return f...
def find_inherited_key_completions(rootpath, root_env): """Return completion keys from INHERITED tuples. Easiest way to get those is to evaluate the tuple, check if it is a CompositeTuple, then enumerate the keys that are NOT in the rightmost tuple. """ tup = inflate_context_tuple(rootpath, root_env) if is...
def find_value_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env): """Find the value of the object under the cursor.""" q = gcl.SourceQuery(filename, line, col) rootpath = ast_tree.find_tokens(q) rootpath = path_until(rootpath, is_thunk) if len(rootpath) <= 1: # Just the file tuple itself...
def add_vec_to_mat(mat, vec, axis=None, inplace=False, target=None, substract=False): """ Add a vector to a matrix """ assert mat.flags.c_contiguous if axis is None: if vec.shape[0] == mat.shape[0]: axis = 0 elif vec.shape[0] == mat.shape[1]: ...
def vector_normalize(mat, max_vec_norm=1.): """ Normalize each column vector in mat to length max_vec_norm if it is longer than max_vec_norm """ assert mat.flags.c_contiguous n, m = mat.shape vector_normalize_kernel.prepared_call( (m, 1, 1), (32, 1, 1), mat.gpudata, np.f...
def preprocess(string): """ Preprocesses a string, by replacing ${VARNAME} with os.environ['VARNAME'] Parameters ---------- string: the str object to preprocess Returns ------- the preprocessed string """ split = string.split('${') rval = [split[0]] for candidate...
def tokenize_by_number(s): """ splits a string into a list of tokens each is either a string containing no numbers or a float """ r = find_number(s) if r == None: return [ s ] else: tokens = [] if r[0] > 0: tokens.append(s[0:r[0]]) tokens.app...
def number_aware_alphabetical_cmp(str1, str2): """ cmp function for sorting a list of strings by alphabetical order, but with numbers sorted numerically. i.e., foo1, foo2, foo10, foo11 instead of foo1, foo10 """ def flatten_tokens(tokens): l = [] for token in tokens...
def match(wrong, candidates): """ wrong: a mispelling candidates: a set of correct words returns a guess of which candidate is the right one This should be used with a small number of candidates and a high potential edit distance. ie, use it to correct a wrong filen...
def censor_non_alphanum(s): """ Returns s with all non-alphanumeric characters replaced with * """ def censor(ch): if (ch >= 'A' and ch <= 'z') or (ch >= '0' and ch <= '9'): return ch return '*' return ''.join([censor(ch) for ch in s])
def is_period_alias(period): """ Check if a given period is possibly an alias. Parameters ---------- period : float A period to test if it is a possible alias or not. Returns ------- is_alias : boolean True if the given period is in a range of period alias. """ ...
def save(filepath, obj, on_overwrite = 'ignore'): """ Serialize `object` to a file denoted by `filepath`. Parameters ---------- filepath : str A filename. If the suffix is `.joblib` and joblib can be imported, `joblib.dump` is used in place of the regular pickling mechanisms...
def get_pickle_protocol(): """ Allow configuration of the pickle protocol on a per-machine basis. This way, if you use multiple platforms with different versions of pickle, you can configure each of them to use the highest protocol supported by all of the machines that you want to be able to com...
def load_train_file(config_file_path): """Loads and parses a yaml file for a Train object. Publishes the relevant training environment variables""" from pylearn2.config import yaml_parse suffix_to_strip = '.yaml' # publish environment variables related to file name if config_file_path.endswith...
def feed_forward(self, input_data, prediction=False): """Propagate forward through the layer Parameters: input_data : ``GPUArray`` Inpute data to perform dropout on. prediction : bool, optional Whether to use prediction model. If true, then the data is ...
def backprop(self, input_data, df_output, cache=None): """ Backpropagate through the hidden layer Parameters: input_data : ``GPUArray`` Inpute data to perform dropout on. df_output : ``GPUArray`` Gradients with respect to the output of this layer ...
def POINTER(obj): """ Create ctypes pointer to object. Notes ----- This function converts None to a real NULL pointer because of bug in how ctypes handles None on 64-bit platforms. """ p = ctypes.POINTER(obj) if not isinstance(p.from_param, classmethod): def from_param(cls...
def gpuarray_ptr(g): """ Return ctypes pointer to data in GPUAarray object. """ addr = int(g.gpudata) if g.dtype == np.int8: return ctypes.cast(addr, POINTER(ctypes.c_byte)) if g.dtype == np.uint8: return ctypes.cast(addr, POINTER(ctypes.c_ubyte)) if g.dtype == np.int16: ...
def cudaMalloc(count, ctype=None): """ Allocate device memory. Allocate memory on the device associated with the current active context. Parameters ---------- count : int Number of bytes of memory to allocate ctype : _ctypes.SimpleType, optional ctypes type to cast retu...
def cudaMallocPitch(pitch, rows, cols, elesize): """ Allocate pitched device memory. Allocate pitched memory on the device associated with the current active context. Parameters ---------- pitch : int Pitch for allocation. rows : int Requested pitched allocation height....
def cudaMemcpy_htod(dst, src, count): """ Copy memory from host to device. Copy data from host memory to device memory. Parameters ---------- dst : ctypes pointer Device memory pointer. src : ctypes pointer Host memory pointer. count : int Number of bytes to cop...
def cudaMemcpy_dtoh(dst, src, count): """ Copy memory from device to host. Copy data from device memory to host memory. Parameters ---------- dst : ctypes pointer Host memory pointer. src : ctypes pointer Device memory pointer. count : int Number of bytes to cop...
def cudaMemGetInfo(): """ Return the amount of free and total device memory. Returns ------- free : long Free memory in bytes. total : long Total memory in bytes. """ free = ctypes.c_size_t() total = ctypes.c_size_t() status = _libcudart.cudaMemGetInfo(ctypes.b...
def cudaGetDevice(): """ Get current CUDA device. Return the identifying number of the device currently used to process CUDA operations. Returns ------- dev : int Device number. """ dev = ctypes.c_int() status = _libcudart.cudaGetDevice(ctypes.byref(dev)) cudaChec...
def cudaDriverGetVersion(): """ Get installed CUDA driver version. Return the version of the installed CUDA driver as an integer. If no driver is detected, 0 is returned. Returns ------- version : int Driver version. """ version = ctypes.c_int() status = _libcudart.cu...
def cudaPointerGetAttributes(ptr): """ Get memory pointer attributes. Returns attributes of the specified pointer. Parameters ---------- ptr : ctypes pointer Memory pointer to examine. Returns ------- memory_type : int Memory type; 1 indicates host memory, 2 indica...
def eval(thunk, env): """Evaluate a thunk in an environment. Will defer the actual evaluation to the thunk itself, but adds two things: caching and recursion detection. Since we have to use a global evaluation stack (because there is a variety of functions that may be invoked, not just eval() but also __get...
def get_node(self, key): """Delegate to our current "value provider" for the node belonging to this key.""" if key in self.names: return self.values.get_member_node(key) if hasattr(self.values, 'get_member_node') else None return self.parent.get_node(key)
def create_table(cls): """ create_table Manually create a temporary table for model in test data base. :return: """ schema_editor = getattr(connection, 'schema_editor', None) if schema_editor: with schema_editor() as schema_editor: sch...
def delete_table(cls): """ delete_table Manually delete a temporary table for model in test data base. :return: """ schema_editor = getattr(connection, 'schema_editor', None) if schema_editor: with connection.schema_editor() as schema_editor: ...
def fake_me(cls, source): """ fake_me Class or method decorator Class decorator: create temporary table for all tests in SimpleTestCase. Method decorator: create temporary model only for given test method. :param source: SimpleTestCase or test function :return: ...
def vcr(decorated_func=None, debug=False, overwrite=False, disabled=False, playback_only=False, tape_name=None): """ Decorator for capturing and simulating network communication ``debug`` : bool, optional Enables debug mode. ``overwrite`` : bool, optional Will run vcr in recordi...
def reset(cls): """ Reset to default settings """ cls.debug = False cls.disabled = False cls.overwrite = False cls.playback_only = False cls.recv_timeout = 5 cls.recv_endmarkers = [] cls.recv_size = None
def to_python(value, seen=None): """Reify values to their Python equivalents. Does recursion detection, failing when that happens. """ seen = seen or set() if isinstance(value, framework.TupleLike): if value.ident in seen: raise RecursionException('to_python: infinite recursion while evaluating %r'...
def walk(value, walker, path=None, seen=None): """Walks the _evaluated_ tree of the given GCL tuple. The appropriate methods of walker will be invoked for every element in the tree. """ seen = seen or set() path = path or [] # Recursion if id(value) in seen: walker.visitRecursion(path) return ...
def fingerprint(value): """Return a hash value that uniquely identifies the GCL value.""" h = hashlib.sha256() _digest(value, h) return h.digest().encode('hex')
def compact_error(err): """Return the the last 2 error messages from an error stack. These error messages turns out to be the most descriptive. """ def err2(e): if isinstance(e, exceptions.EvaluationError) and e.inner: message, i = err2(e.inner) if i == 1: return ', '.join([e.args[0], s...
def backprop(self, input_data, targets, cache=None): """ Backpropagate through the logistic layer. Parameters: input_data : ``GPUArray`` Inpute data to compute activations for. targets : ``GPUArray`` The target values of the units. ...
def cross_entropy_error(self, input_data, targets, average=True, cache=None, prediction=False): """ Return the cross entropy error """ if cache is not None: activations = cache else: activations = \ self.feed_forward(inpu...
def stylize_comment_block(lines): """Parse comment lines and make subsequent indented lines into a code block block. """ normal, sep, in_code = range(3) state = normal for line in lines: indented = line.startswith(' ') empty_line = line.strip() == '' if state == normal and empty_line: ...
def sort_members(tup, names): """Return two pairs of members, scalar and tuple members. The scalars will be sorted s.t. the unbound members are at the top. """ scalars, tuples = partition(lambda x: not is_tuple_node(tup.member[x].value), names) unbound, bound = partition(lambda x: tup.member[x].value.is_unbo...
def resolve_file(fname, paths): """Resolve filename relatively against one of the given paths, if possible.""" fpath = path.abspath(fname) for p in paths: spath = path.abspath(p) if fpath.startswith(spath): return fpath[len(spath) + 1:] return fname
def generate(self): """Generate a list of strings representing the table in RST format.""" header = ' '.join('=' * self.width[i] for i in range(self.w)) lines = [ ' '.join(row[i].ljust(self.width[i]) for i in range(self.w)) for row in self.rows] return [header] + lines + [header]
def partition(pred, iterable): 'Use a predicate to partition entries into false entries and true entries' # partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9 t1, t2 = itertools.tee(iterable) return list(filter(negate(pred), t1)), list(filter(pred, t2))
def select(self, model): """Select nodes according to the input selector. This can ALWAYS return multiple root elements. """ res = [] def doSelect(value, pre, remaining): if not remaining: res.append((pre, value)) else: # For the other selectors to work, value must be a...
def deep(self): """Return a deep dict of the values selected. The leaf values may still be gcl Tuples. Use util.to_python() if you want to reify everything to real Python values. """ self.lists = {} ret = {} for path, value in self.paths_values(): self.recursiveSet(ret, path, value) ...
def ldSet(self, what, key, value): """List/dictionary-aware set.""" if isListKey(key): # Make sure we keep the indexes consistent, insert missing_values # as necessary. We do remember the lists, so that we can remove # missing values after inserting all values from all selectors. self.li...
def ldGet(self, what, key): """List-aware get.""" if isListKey(key): return what[listKeyIndex(key)] else: return what[key]
def ldContains(self, what, key): """List/dictinary/missing-aware contains. If the value is a "missing_value", we'll treat it as non-existent so it will be overwritten by an empty list/dict when necessary to assign child keys. """ if isListKey(key): i = listKeyIndex(key) return i < l...
def find_recursive_dependency(self): """Return a list of nodes that have a recursive dependency.""" nodes_on_path = [] def helper(nodes): for node in nodes: cycle = node in nodes_on_path nodes_on_path.append(node) if cycle or helper(self.deps.get(node, [])): return T...
def enterTuple(self, tuple, path): """Called for every tuple. If this returns False, the elements of the tuple will not be recursed over and leaveTuple() will not be called. """ if skip_name(path): return False node = Node(path, tuple) if self.condition.matches(node): self.unord...
def convertAndMake(converter, handler): """Convert with location.""" def convertAction(loc, value): return handler(loc, converter(value)) return convertAction
def mkApplications(location, *atoms): """Make a sequence of applications from a list of tokens. atoms is a list of atoms, which will be handled left-associatively. E.g: ['foo', [], []] == foo()() ==> Application(Application('foo', []), []) """ atoms = list(atoms) while len(atoms) > 1: atoms[0:2] =...
def call_fn(fn, arglist, env): """Call a function, respecting all the various types of functions that exist.""" if isinstance(fn, framework.LazyFunction): # The following looks complicated, but this is necessary because you can't # construct closures over the loop variable directly. thunks = [(lambda th...
def schema_spec_from_tuple(tup): """Return the schema spec from a run-time tuple.""" if hasattr(tup, 'get_schema_spec'): # Tuples have a TupleSchema field that contains a model of the schema return schema.from_spec({ 'fields': TupleSchemaAccess(tup), 'required': tup.get_required_fields()}) ...
def make_schema_from(value, env): """Make a Schema object from the given spec. The input and output types of this function are super unclear, and are held together by ponies, wishes, duct tape, and a load of tests. See the comments for horrific entertainment. """ # So this thing may not need to evaluate any...
def bracketedList(l, r, sep, expr, allow_missing_close=False): """Parse bracketed list. Empty list is possible, as is a trailing separator. """ # We may need to backtrack for lists, because of list comprehension, but not for # any of the other lists strict = l != '[' closer = sym(r) if not allow_missing_...
def unquote(s): """Unquote the indicated string.""" # Ignore the left- and rightmost chars (which should be quotes). # Use the Python engine to decode the escape sequence i, N = 1, len(s) - 1 ret = [] while i < N: if s[i] == '\\' and i < N - 1: ret.append(UNQUOTE_MAP.get(s[i+1], s[i+1])) i +...
def pattern(name, pattern): """Function to put a name on a pyparsing pattern. Just for ease of debugging/tracing parse errors. """ pattern.setName(name) astracing.maybe_trace(pattern) return pattern
def make_grammar(allow_errors): """Make the part of the grammar that depends on whether we swallow errors or not.""" if allow_errors in GRAMMAR_CACHE: return GRAMMAR_CACHE[allow_errors] tuple = p.Forward() catch_errors = p.Forward() catch_errors << (p.Regex('[^{};]*') - p.Optional(tuple) - p.Regex('[^;}]...
def reads(s, filename, loader, implicit_tuple, allow_errors): """Load but don't evaluate a GCL expression from a string.""" try: the_context.filename = filename the_context.loader = loader grammar = make_grammar(allow_errors=allow_errors) root = grammar.start_tuple if implicit_tuple else grammar.st...
def find_tokens(self, q): """Find all AST nodes at the given filename, line and column.""" found_me = [] if hasattr(self, 'location'): if self.location.contains(q): found_me = [self] elif self._found_by(q): found_me = [self] cs = [n.find_tokens(q) for n in self._children()] ...
def _make_tuple(self, env): """Instantiate the Tuple based on this TupleNode.""" t = runtime.Tuple(self, env, dict2tuple) # A tuple also provides its own schema spec schema = schema_spec_from_tuple(t) t.attach_schema(schema) return t
def applyTuple(self, tuple, right, env): """Apply a tuple to something else.""" if len(right) != 1: raise exceptions.EvaluationError('Tuple (%r) can only be applied to one argument, got %r' % (self.left, self.right)) right = right[0] return tuple(right)
def applyIndex(self, lst, right): """Apply a list to something else.""" if len(right) != 1: raise exceptions.EvaluationError('%r can only be applied to one argument, got %r' % (self.left, self.right)) right = right[0] if isinstance(right, int): return lst[right] raise exceptions.Evalua...
def pre_gradient_update(self): """ First step of Nesterov momentum method: take step in direction of accumulated gradient """ updates = zip(self.velocity, self.model.n_parameters * [1.]) self.model.update_parameters(updates)
def class_error(self, input_data, targets, average=True, cache=None, prediction=False): """ Return the classification error rate """ if cache is not None: activations = cache else: activations = \ self.feed_forward(input_data, pr...
def kl_error(self, input_data, targets, average=True, cache=None, prediction=True): """ The KL divergence error """ if cache is not None: activations = cache else: activations = \ self.feed_forward(input_data, prediction=prediction)...
def dot(x_gpu, y_gpu, transa='N', transb='N', handle=None, target=None): """ Dot product of two arrays. For 1D arrays, this function computes the inner product. For 2D arrays of shapes `(m, k)` and `(k, n)`, it computes the matrix product; the result has shape `(m, n)`. Parameters --------...
def make_tempfile(data=None): "Create a temp file, write our PID into it." with tempfile.NamedTemporaryFile(mode='w', delete=False) as temp: temp.write(six.text_type(data if data is not None else os.getpid())) return temp.name
def parameters(self): """Return a list where each element contains the parameters for a task. """ parameters = [] for task in self.tasks: parameters.extend(task.parameters) return parameters
def parameters(self, value): """Update the parameters. ``value`` must be a list/tuple of length ``MultitaskTopLayer.n_tasks``, each element of which must have the correct number of parameters for the task. """ assert len(value) == self.n_parameters i = 0 ...
def feed_forward(self, input_data, prediction=False): """Call ``feed_forward`` for each task and combine the activations. Passes ``input_data`` to all tasks and returns the activations as a list. Parameters: input_data : ``GPUArray`` Inpute data to compute ...
def backprop(self, input_data, targets, cache=None): """Compute gradients for each task and combine the results. Parameters: input_data : ``GPUArray`` Inpute data to compute activations for. targets : ``GPUArray`` The target values of the units. ca...
def cross_entropy_error(self, input_data, targets, average=True, cache=None, prediction=False, sum_errors=True): """ Computes the cross-entropy error for all tasks. """ loss = [] if cache is None: cache = self.n_tasks *...
def parameters(self, value): """Update the parameters. ``value`` must have the shape ``(weights, biases)``""" self.W = value[0] if isinstance(value[0], GPUArray) else \ gpuarray.to_gpu(value[0]) self.b = value[1] if isinstance(value[0], GPUArray) else \ gpuarray.to_gp...
def architecture(self): """Returns a dictionary describing the architecture of the layer.""" arch = {'class': self.__class__, 'n_in': self.n_in, 'n_units': self.n_units, 'activation_function': self.activation_function if hasattr(self, 'acti...
def feed_forward(self, input_data, prediction=False): """Propagate forward through the layer Parameters: input_data : ``GPUArray`` Input data to compute activations for. prediction : bool, optional Whether to use prediction model. Only relevant when using ...
def backprop(self, input_data, df_output, cache=None): """ Backpropagate through the hidden layer Parameters: input_data : ``GPUArray`` Input data to compute activations for. df_output : ``GPUArray`` Gradients with respect to the activations of this layer ...
def cublasCreate(): """ Initialize CUBLAS. Initializes CUBLAS and creates a handle to a structure holding the CUBLAS library context. Returns ------- handle : void_p CUBLAS context. """ handle = ctypes.c_void_p() status = _libcublas.cublasCreate_v2(ctypes....
def cublasDestroy(handle): """ Release CUBLAS resources. Releases hardware resources used by CUBLAS. Parameters ---------- handle : void_p CUBLAS context. """ status = _libcublas.cublasDestroy_v2(ctypes.c_void_p(handle)) cublasCheckStatus(status)
def cublasGetVersion(handle): """ Get CUBLAS version. Returns version number of installed CUBLAS libraries. Parameters ---------- handle : void_p CUBLAS context. Returns ------- version : int CUBLAS version. """ version = ctypes.c_int() status = _...
def cublasSetStream(handle, id): """ Set current CUBLAS library stream. Parameters ---------- handle : id CUBLAS context. id : int Stream ID. """ status = _libcublas.cublasSetStream_v2(handle, id) cublasCheckStatus(status)
def cublasGetStream(handle): """ Set current CUBLAS library stream. Parameters ---------- handle : void_p CUBLAS context. Returns ------- id : int Stream ID. """ id = ctypes.c_int() status = _libcublas.cublasGetStream_v2(handle, ctypes.byref(id)) ...
def cublasSgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for real general banded matrix. """ status = _libcublas.cublasSgbmv_v2(handle, trans, m, n, kl, ku, ...
def cublasCgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for complex general banded matrix. """ status = _libcublas.cublasCgbmv_v2(handle, trans, m, n, kl, ku, ...
def cublasZgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for complex general banded matrix. """ status = _libcublas.cublasZgbmv_v2(handle, trans, m, n, kl, ku, ...
def cublasSgemv(handle, trans, m, n, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for real general matrix. """ status = _libcublas.cublasSgemv_v2(handle, _CUBLAS_OP[trans], m, n, ctypes.byref(ctypes.c_fl...

def half_mag_amplitude_ratio(self, mag, avg, weight): """ Return ratio of amplitude of higher and lower magnitudes. A ratio of amplitude of higher and lower magnitudes than average, considering weights. This ratio, by definition, should be higher for EB than for others. ...

def half_mag_amplitude_ratio2(self, mag, avg): """ Return ratio of amplitude of higher and lower magnitudes. A ratio of amplitude of higher and lower magnitudes than average, considering weights. This ratio, by definition, should be higher for EB than for others. Param...

def get_eta(self, mag, std): """ Return Eta feature. Parameters ---------- mag : array_like An array of magnitudes. std : array_like A standard deviation of magnitudes. Returns ------- eta : float The value of ...

def slope_percentile(self, date, mag): """ Return 10% and 90% percentile of slope. Parameters ---------- date : array_like An array of phase-folded date. Sorted. mag : array_like An array of phase-folded magnitudes. Sorted by date. Return...

def get_cusum(self, mag): """ Return max - min of cumulative sum. Parameters ---------- mag : array_like An array of magnitudes. Returns ------- mm_cusum : float Max - min of cumulative sum. """ c = np.cumsum(mag ...

def get_features2(self): """ Return all features with its names. Returns ------- names : list Feature names. values : list Feature values """ feature_names = [] feature_values = [] # Get all the names of features....

def get_features_all(self): """ Return all features with its names. Regardless of being used for train and prediction. Sorted by the names. Returns ------- all_features : OrderedDict Features dictionary. """ features = {} # Get all ...

def init(device_id=None, random_seed=None): """Initialize Hebel. This function creates a CUDA context, CUBLAS context and initializes and seeds the pseudo-random number generator. **Parameters:** device_id : integer, optional The ID of the GPU device to use. If this is omitted, PyCUDA...

def inflate_context_tuple(ast_rootpath, root_env): """Instantiate a Tuple from a TupleNode. Walking the AST tree upwards, evaluate from the root down again. """ with util.LogTime('inflate_context_tuple'): # We only need to look at tuple members going down. inflated = ast_rootpath[0].eval(root_env) ...

def enumerate_scope(ast_rootpath, root_env=None, include_default_builtins=False): """Return a dict of { name => Completions } for the given tuple node. Enumerates all keys that are in scope in a given tuple. The node part of the tuple may be None, in case the binding is a built-in. """ with util.LogTime('enu...

def find_deref_completions(ast_rootpath, root_env=gcl.default_env): """Returns a dict of { name => Completions }.""" with util.LogTime('find_deref_completions'): tup = inflate_context_tuple(ast_rootpath, root_env) path = path_until(ast_rootpath, is_deref_node) if not path: return {} deref = pa...

def is_identifier_position(rootpath): """Return whether the cursor is in identifier-position in a member declaration.""" if len(rootpath) >= 2 and is_tuple_member_node(rootpath[-2]) and is_identifier(rootpath[-1]): return True if len(rootpath) >= 1 and is_tuple_node(rootpath[-1]): # No deeper node than tu...

def find_completions_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env): """Find completions at the cursor. Return a dict of { name => Completion } objects. """ q = gcl.SourceQuery(filename, line, col - 1) rootpath = ast_tree.find_tokens(q) if is_identifier_position(rootpath): return f...

def find_inherited_key_completions(rootpath, root_env): """Return completion keys from INHERITED tuples. Easiest way to get those is to evaluate the tuple, check if it is a CompositeTuple, then enumerate the keys that are NOT in the rightmost tuple. """ tup = inflate_context_tuple(rootpath, root_env) if is...

def find_value_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env): """Find the value of the object under the cursor.""" q = gcl.SourceQuery(filename, line, col) rootpath = ast_tree.find_tokens(q) rootpath = path_until(rootpath, is_thunk) if len(rootpath) <= 1: # Just the file tuple itself...

def add_vec_to_mat(mat, vec, axis=None, inplace=False, target=None, substract=False): """ Add a vector to a matrix """ assert mat.flags.c_contiguous if axis is None: if vec.shape[0] == mat.shape[0]: axis = 0 elif vec.shape[0] == mat.shape[1]: ...

def vector_normalize(mat, max_vec_norm=1.): """ Normalize each column vector in mat to length max_vec_norm if it is longer than max_vec_norm """ assert mat.flags.c_contiguous n, m = mat.shape vector_normalize_kernel.prepared_call( (m, 1, 1), (32, 1, 1), mat.gpudata, np.f...

def preprocess(string): """ Preprocesses a string, by replacing ${VARNAME} with os.environ['VARNAME'] Parameters ---------- string: the str object to preprocess Returns ------- the preprocessed string """ split = string.split('${') rval = [split[0]] for candidate...

def tokenize_by_number(s): """ splits a string into a list of tokens each is either a string containing no numbers or a float """ r = find_number(s) if r == None: return [ s ] else: tokens = [] if r[0] > 0: tokens.append(s[0:r[0]]) tokens.app...

def number_aware_alphabetical_cmp(str1, str2): """ cmp function for sorting a list of strings by alphabetical order, but with numbers sorted numerically. i.e., foo1, foo2, foo10, foo11 instead of foo1, foo10 """ def flatten_tokens(tokens): l = [] for token in tokens...

def match(wrong, candidates): """ wrong: a mispelling candidates: a set of correct words returns a guess of which candidate is the right one This should be used with a small number of candidates and a high potential edit distance. ie, use it to correct a wrong filen...

def censor_non_alphanum(s): """ Returns s with all non-alphanumeric characters replaced with * """ def censor(ch): if (ch >= 'A' and ch <= 'z') or (ch >= '0' and ch <= '9'): return ch return '*' return ''.join([censor(ch) for ch in s])

def is_period_alias(period): """ Check if a given period is possibly an alias. Parameters ---------- period : float A period to test if it is a possible alias or not. Returns ------- is_alias : boolean True if the given period is in a range of period alias. """ ...

def save(filepath, obj, on_overwrite = 'ignore'): """ Serialize `object` to a file denoted by `filepath`. Parameters ---------- filepath : str A filename. If the suffix is `.joblib` and joblib can be imported, `joblib.dump` is used in place of the regular pickling mechanisms...

def get_pickle_protocol(): """ Allow configuration of the pickle protocol on a per-machine basis. This way, if you use multiple platforms with different versions of pickle, you can configure each of them to use the highest protocol supported by all of the machines that you want to be able to com...

def load_train_file(config_file_path): """Loads and parses a yaml file for a Train object. Publishes the relevant training environment variables""" from pylearn2.config import yaml_parse suffix_to_strip = '.yaml' # publish environment variables related to file name if config_file_path.endswith...

def feed_forward(self, input_data, prediction=False): """Propagate forward through the layer **Parameters:** input_data : ``GPUArray`` Inpute data to perform dropout on. prediction : bool, optional Whether to use prediction model. If true, then the data is ...

def backprop(self, input_data, df_output, cache=None): """ Backpropagate through the hidden layer **Parameters:** input_data : ``GPUArray`` Inpute data to perform dropout on. df_output : ``GPUArray`` Gradients with respect to the output of this layer ...

def POINTER(obj): """ Create ctypes pointer to object. Notes ----- This function converts None to a real NULL pointer because of bug in how ctypes handles None on 64-bit platforms. """ p = ctypes.POINTER(obj) if not isinstance(p.from_param, classmethod): def from_param(cls...

def gpuarray_ptr(g): """ Return ctypes pointer to data in GPUAarray object. """ addr = int(g.gpudata) if g.dtype == np.int8: return ctypes.cast(addr, POINTER(ctypes.c_byte)) if g.dtype == np.uint8: return ctypes.cast(addr, POINTER(ctypes.c_ubyte)) if g.dtype == np.int16: ...

def cudaMalloc(count, ctype=None): """ Allocate device memory. Allocate memory on the device associated with the current active context. Parameters ---------- count : int Number of bytes of memory to allocate ctype : _ctypes.SimpleType, optional ctypes type to cast retu...

def cudaMallocPitch(pitch, rows, cols, elesize): """ Allocate pitched device memory. Allocate pitched memory on the device associated with the current active context. Parameters ---------- pitch : int Pitch for allocation. rows : int Requested pitched allocation height....

def cudaMemcpy_htod(dst, src, count): """ Copy memory from host to device. Copy data from host memory to device memory. Parameters ---------- dst : ctypes pointer Device memory pointer. src : ctypes pointer Host memory pointer. count : int Number of bytes to cop...

def cudaMemcpy_dtoh(dst, src, count): """ Copy memory from device to host. Copy data from device memory to host memory. Parameters ---------- dst : ctypes pointer Host memory pointer. src : ctypes pointer Device memory pointer. count : int Number of bytes to cop...

def cudaMemGetInfo(): """ Return the amount of free and total device memory. Returns ------- free : long Free memory in bytes. total : long Total memory in bytes. """ free = ctypes.c_size_t() total = ctypes.c_size_t() status = _libcudart.cudaMemGetInfo(ctypes.b...

def cudaGetDevice(): """ Get current CUDA device. Return the identifying number of the device currently used to process CUDA operations. Returns ------- dev : int Device number. """ dev = ctypes.c_int() status = _libcudart.cudaGetDevice(ctypes.byref(dev)) cudaChec...

def cudaDriverGetVersion(): """ Get installed CUDA driver version. Return the version of the installed CUDA driver as an integer. If no driver is detected, 0 is returned. Returns ------- version : int Driver version. """ version = ctypes.c_int() status = _libcudart.cu...

def cudaPointerGetAttributes(ptr): """ Get memory pointer attributes. Returns attributes of the specified pointer. Parameters ---------- ptr : ctypes pointer Memory pointer to examine. Returns ------- memory_type : int Memory type; 1 indicates host memory, 2 indica...

def eval(thunk, env): """Evaluate a thunk in an environment. Will defer the actual evaluation to the thunk itself, but adds two things: caching and recursion detection. Since we have to use a global evaluation stack (because there is a variety of functions that may be invoked, not just eval() but also __get...

def get_node(self, key): """Delegate to our current "value provider" for the node belonging to this key.""" if key in self.names: return self.values.get_member_node(key) if hasattr(self.values, 'get_member_node') else None return self.parent.get_node(key)

def create_table(cls): """ create_table Manually create a temporary table for model in test data base. :return: """ schema_editor = getattr(connection, 'schema_editor', None) if schema_editor: with schema_editor() as schema_editor: sch...

def delete_table(cls): """ delete_table Manually delete a temporary table for model in test data base. :return: """ schema_editor = getattr(connection, 'schema_editor', None) if schema_editor: with connection.schema_editor() as schema_editor: ...

def fake_me(cls, source): """ fake_me Class or method decorator Class decorator: create temporary table for all tests in SimpleTestCase. Method decorator: create temporary model only for given test method. :param source: SimpleTestCase or test function :return: ...

def vcr(decorated_func=None, debug=False, overwrite=False, disabled=False, playback_only=False, tape_name=None): """ Decorator for capturing and simulating network communication ``debug`` : bool, optional Enables debug mode. ``overwrite`` : bool, optional Will run vcr in recordi...

def reset(cls): """ Reset to default settings """ cls.debug = False cls.disabled = False cls.overwrite = False cls.playback_only = False cls.recv_timeout = 5 cls.recv_endmarkers = [] cls.recv_size = None

def to_python(value, seen=None): """Reify values to their Python equivalents. Does recursion detection, failing when that happens. """ seen = seen or set() if isinstance(value, framework.TupleLike): if value.ident in seen: raise RecursionException('to_python: infinite recursion while evaluating %r'...

def walk(value, walker, path=None, seen=None): """Walks the _evaluated_ tree of the given GCL tuple. The appropriate methods of walker will be invoked for every element in the tree. """ seen = seen or set() path = path or [] # Recursion if id(value) in seen: walker.visitRecursion(path) return ...

def fingerprint(value): """Return a hash value that uniquely identifies the GCL value.""" h = hashlib.sha256() _digest(value, h) return h.digest().encode('hex')

def compact_error(err): """Return the the last 2 error messages from an error stack. These error messages turns out to be the most descriptive. """ def err2(e): if isinstance(e, exceptions.EvaluationError) and e.inner: message, i = err2(e.inner) if i == 1: return ', '.join([e.args[0], s...

def backprop(self, input_data, targets, cache=None): """ Backpropagate through the logistic layer. **Parameters:** input_data : ``GPUArray`` Inpute data to compute activations for. targets : ``GPUArray`` The target values of the units. ...

def cross_entropy_error(self, input_data, targets, average=True, cache=None, prediction=False): """ Return the cross entropy error """ if cache is not None: activations = cache else: activations = \ self.feed_forward(inpu...

def stylize_comment_block(lines): """Parse comment lines and make subsequent indented lines into a code block block. """ normal, sep, in_code = range(3) state = normal for line in lines: indented = line.startswith(' ') empty_line = line.strip() == '' if state == normal and empty_line: ...

def sort_members(tup, names): """Return two pairs of members, scalar and tuple members. The scalars will be sorted s.t. the unbound members are at the top. """ scalars, tuples = partition(lambda x: not is_tuple_node(tup.member[x].value), names) unbound, bound = partition(lambda x: tup.member[x].value.is_unbo...

def resolve_file(fname, paths): """Resolve filename relatively against one of the given paths, if possible.""" fpath = path.abspath(fname) for p in paths: spath = path.abspath(p) if fpath.startswith(spath): return fpath[len(spath) + 1:] return fname

def generate(self): """Generate a list of strings representing the table in RST format.""" header = ' '.join('=' * self.width[i] for i in range(self.w)) lines = [ ' '.join(row[i].ljust(self.width[i]) for i in range(self.w)) for row in self.rows] return [header] + lines + [header]

def partition(pred, iterable): 'Use a predicate to partition entries into false entries and true entries' # partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9 t1, t2 = itertools.tee(iterable) return list(filter(negate(pred), t1)), list(filter(pred, t2))

def select(self, model): """Select nodes according to the input selector. This can ALWAYS return multiple root elements. """ res = [] def doSelect(value, pre, remaining): if not remaining: res.append((pre, value)) else: # For the other selectors to work, value must be a...

def deep(self): """Return a deep dict of the values selected. The leaf values may still be gcl Tuples. Use util.to_python() if you want to reify everything to real Python values. """ self.lists = {} ret = {} for path, value in self.paths_values(): self.recursiveSet(ret, path, value) ...

def ldSet(self, what, key, value): """List/dictionary-aware set.""" if isListKey(key): # Make sure we keep the indexes consistent, insert missing_values # as necessary. We do remember the lists, so that we can remove # missing values after inserting all values from all selectors. self.li...

def ldGet(self, what, key): """List-aware get.""" if isListKey(key): return what[listKeyIndex(key)] else: return what[key]

def ldContains(self, what, key): """List/dictinary/missing-aware contains. If the value is a "missing_value", we'll treat it as non-existent so it will be overwritten by an empty list/dict when necessary to assign child keys. """ if isListKey(key): i = listKeyIndex(key) return i < l...

def find_recursive_dependency(self): """Return a list of nodes that have a recursive dependency.""" nodes_on_path = [] def helper(nodes): for node in nodes: cycle = node in nodes_on_path nodes_on_path.append(node) if cycle or helper(self.deps.get(node, [])): return T...

def enterTuple(self, tuple, path): """Called for every tuple. If this returns False, the elements of the tuple will not be recursed over and leaveTuple() will not be called. """ if skip_name(path): return False node = Node(path, tuple) if self.condition.matches(node): self.unord...

def convertAndMake(converter, handler): """Convert with location.""" def convertAction(loc, value): return handler(loc, converter(value)) return convertAction

def mkApplications(location, *atoms): """Make a sequence of applications from a list of tokens. atoms is a list of atoms, which will be handled left-associatively. E.g: ['foo', [], []] == foo()() ==> Application(Application('foo', []), []) """ atoms = list(atoms) while len(atoms) > 1: atoms[0:2] =...

def call_fn(fn, arglist, env): """Call a function, respecting all the various types of functions that exist.""" if isinstance(fn, framework.LazyFunction): # The following looks complicated, but this is necessary because you can't # construct closures over the loop variable directly. thunks = [(lambda th...

def schema_spec_from_tuple(tup): """Return the schema spec from a run-time tuple.""" if hasattr(tup, 'get_schema_spec'): # Tuples have a TupleSchema field that contains a model of the schema return schema.from_spec({ 'fields': TupleSchemaAccess(tup), 'required': tup.get_required_fields()}) ...

def make_schema_from(value, env): """Make a Schema object from the given spec. The input and output types of this function are super unclear, and are held together by ponies, wishes, duct tape, and a load of tests. See the comments for horrific entertainment. """ # So this thing may not need to evaluate any...

def bracketedList(l, r, sep, expr, allow_missing_close=False): """Parse bracketed list. Empty list is possible, as is a trailing separator. """ # We may need to backtrack for lists, because of list comprehension, but not for # any of the other lists strict = l != '[' closer = sym(r) if not allow_missing_...

def unquote(s): """Unquote the indicated string.""" # Ignore the left- and rightmost chars (which should be quotes). # Use the Python engine to decode the escape sequence i, N = 1, len(s) - 1 ret = [] while i < N: if s[i] == '\\' and i < N - 1: ret.append(UNQUOTE_MAP.get(s[i+1], s[i+1])) i +...

def pattern(name, pattern): """Function to put a name on a pyparsing pattern. Just for ease of debugging/tracing parse errors. """ pattern.setName(name) astracing.maybe_trace(pattern) return pattern

def make_grammar(allow_errors): """Make the part of the grammar that depends on whether we swallow errors or not.""" if allow_errors in GRAMMAR_CACHE: return GRAMMAR_CACHE[allow_errors] tuple = p.Forward() catch_errors = p.Forward() catch_errors << (p.Regex('[^{};]*') - p.Optional(tuple) - p.Regex('[^;}]...

def reads(s, filename, loader, implicit_tuple, allow_errors): """Load but don't evaluate a GCL expression from a string.""" try: the_context.filename = filename the_context.loader = loader grammar = make_grammar(allow_errors=allow_errors) root = grammar.start_tuple if implicit_tuple else grammar.st...

def find_tokens(self, q): """Find all AST nodes at the given filename, line and column.""" found_me = [] if hasattr(self, 'location'): if self.location.contains(q): found_me = [self] elif self._found_by(q): found_me = [self] cs = [n.find_tokens(q) for n in self._children()] ...

def _make_tuple(self, env): """Instantiate the Tuple based on this TupleNode.""" t = runtime.Tuple(self, env, dict2tuple) # A tuple also provides its own schema spec schema = schema_spec_from_tuple(t) t.attach_schema(schema) return t

def applyTuple(self, tuple, right, env): """Apply a tuple to something else.""" if len(right) != 1: raise exceptions.EvaluationError('Tuple (%r) can only be applied to one argument, got %r' % (self.left, self.right)) right = right[0] return tuple(right)

def applyIndex(self, lst, right): """Apply a list to something else.""" if len(right) != 1: raise exceptions.EvaluationError('%r can only be applied to one argument, got %r' % (self.left, self.right)) right = right[0] if isinstance(right, int): return lst[right] raise exceptions.Evalua...

def pre_gradient_update(self): """ First step of Nesterov momentum method: take step in direction of accumulated gradient """ updates = zip(self.velocity, self.model.n_parameters * [1.]) self.model.update_parameters(updates)

def class_error(self, input_data, targets, average=True, cache=None, prediction=False): """ Return the classification error rate """ if cache is not None: activations = cache else: activations = \ self.feed_forward(input_data, pr...

def kl_error(self, input_data, targets, average=True, cache=None, prediction=True): """ The KL divergence error """ if cache is not None: activations = cache else: activations = \ self.feed_forward(input_data, prediction=prediction)...

def dot(x_gpu, y_gpu, transa='N', transb='N', handle=None, target=None): """ Dot product of two arrays. For 1D arrays, this function computes the inner product. For 2D arrays of shapes `(m, k)` and `(k, n)`, it computes the matrix product; the result has shape `(m, n)`. Parameters --------...

def make_tempfile(data=None): "Create a temp file, write our PID into it." with tempfile.NamedTemporaryFile(mode='w', delete=False) as temp: temp.write(six.text_type(data if data is not None else os.getpid())) return temp.name

def parameters(self): """Return a list where each element contains the parameters for a task. """ parameters = [] for task in self.tasks: parameters.extend(task.parameters) return parameters

def parameters(self, value): """Update the parameters. ``value`` must be a list/tuple of length ``MultitaskTopLayer.n_tasks``, each element of which must have the correct number of parameters for the task. """ assert len(value) == self.n_parameters i = 0 ...

def feed_forward(self, input_data, prediction=False): """Call ``feed_forward`` for each task and combine the activations. Passes ``input_data`` to all tasks and returns the activations as a list. **Parameters:** input_data : ``GPUArray`` Inpute data to compute ...

def backprop(self, input_data, targets, cache=None): """Compute gradients for each task and combine the results. **Parameters:** input_data : ``GPUArray`` Inpute data to compute activations for. targets : ``GPUArray`` The target values of the units. ca...

def cross_entropy_error(self, input_data, targets, average=True, cache=None, prediction=False, sum_errors=True): """ Computes the cross-entropy error for all tasks. """ loss = [] if cache is None: cache = self.n_tasks *...

def parameters(self, value): """Update the parameters. ``value`` must have the shape ``(weights, biases)``""" self.W = value[0] if isinstance(value[0], GPUArray) else \ gpuarray.to_gpu(value[0]) self.b = value[1] if isinstance(value[0], GPUArray) else \ gpuarray.to_gp...

def architecture(self): """Returns a dictionary describing the architecture of the layer.""" arch = {'class': self.__class__, 'n_in': self.n_in, 'n_units': self.n_units, 'activation_function': self.activation_function if hasattr(self, 'acti...

def feed_forward(self, input_data, prediction=False): """Propagate forward through the layer **Parameters:** input_data : ``GPUArray`` Input data to compute activations for. prediction : bool, optional Whether to use prediction model. Only relevant when using ...

def backprop(self, input_data, df_output, cache=None): """ Backpropagate through the hidden layer **Parameters:** input_data : ``GPUArray`` Input data to compute activations for. df_output : ``GPUArray`` Gradients with respect to the activations of this layer ...

def cublasCreate(): """ Initialize CUBLAS. Initializes CUBLAS and creates a handle to a structure holding the CUBLAS library context. Returns ------- handle : void_p CUBLAS context. """ handle = ctypes.c_void_p() status = _libcublas.cublasCreate_v2(ctypes....

def cublasDestroy(handle): """ Release CUBLAS resources. Releases hardware resources used by CUBLAS. Parameters ---------- handle : void_p CUBLAS context. """ status = _libcublas.cublasDestroy_v2(ctypes.c_void_p(handle)) cublasCheckStatus(status)

def cublasGetVersion(handle): """ Get CUBLAS version. Returns version number of installed CUBLAS libraries. Parameters ---------- handle : void_p CUBLAS context. Returns ------- version : int CUBLAS version. """ version = ctypes.c_int() status = _...

def cublasSetStream(handle, id): """ Set current CUBLAS library stream. Parameters ---------- handle : id CUBLAS context. id : int Stream ID. """ status = _libcublas.cublasSetStream_v2(handle, id) cublasCheckStatus(status)

def cublasGetStream(handle): """ Set current CUBLAS library stream. Parameters ---------- handle : void_p CUBLAS context. Returns ------- id : int Stream ID. """ id = ctypes.c_int() status = _libcublas.cublasGetStream_v2(handle, ctypes.byref(id)) ...

def cublasSgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for real general banded matrix. """ status = _libcublas.cublasSgbmv_v2(handle, trans, m, n, kl, ku, ...

def cublasCgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for complex general banded matrix. """ status = _libcublas.cublasCgbmv_v2(handle, trans, m, n, kl, ku, ...

def cublasZgbmv(handle, trans, m, n, kl, ku, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for complex general banded matrix. """ status = _libcublas.cublasZgbmv_v2(handle, trans, m, n, kl, ku, ...

def cublasSgemv(handle, trans, m, n, alpha, A, lda, x, incx, beta, y, incy): """ Matrix-vector product for real general matrix. """ status = _libcublas.cublasSgemv_v2(handle, _CUBLAS_OP[trans], m, n, ctypes.byref(ctypes.c_fl...